2540120585_FinalExam
2540120585_KevinaNugrahaEleeas
2022-07-11
2540120585 - Kevina Nugraha Eleeas
LINK VIDEO: 2540120585_FinalExam.mp4
Import Library
library(readr)## Warning: package 'readr' was built under R version 4.1.3library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.3library(dlookr)##
## Attaching package: 'dlookr'## The following object is masked from 'package:base':
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## transformlibrary(Hmisc)## Warning: package 'Hmisc' was built under R version 4.1.3## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following object is masked from 'package:dlookr':
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## describe## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, unitslibrary(ROCR)## Warning: package 'ROCR' was built under R version 4.1.3library(rpart)## Warning: package 'rpart' was built under R version 4.1.3library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.1.3library(outliers)## Warning: package 'outliers' was built under R version 4.1.3library(rattle)## Warning: package 'rattle' was built under R version 4.1.3## Loading required package: tibble## Loading required package: bitops## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.##
## Attaching package: 'rattle'## The following object is masked from 'package:dlookr':
##
## binninglibrary(patchwork)## Warning: package 'patchwork' was built under R version 4.1.3Import DataSet
path = ('~/KULIAH/SMT 2/Data Mining & Visualization/[LEC]_UAS_DTSC6005001_Data Mining and Visualization/heartData.csv')
heartData <- read.csv(path)
#View(heartData)
#head(heartData)
viewDataFrame <- heartData
colnames(viewDataFrame) <- c('Age', 'Sex', 'Chest Pain Type', 'Resting Blood Pressure', 'Cholesterol', 'Fasting Blood Sugar', 'Resting ECG', 'Max Heart Rate', 'Exercise Angina', 'Oldpeak', 'ST Slope', 'Heart Disease')
head(viewDataFrame)## Age Sex Chest Pain Type Resting Blood Pressure Cholesterol
## 1 40 M ATA 140 289
## 2 49 F NAP 160 180
## 3 37 M ATA 130 283
## 4 48 F ASY 138 214
## 5 54 M NAP 150 195
## 6 39 M NAP 120 339
## Fasting Blood Sugar Resting ECG Max Heart Rate Exercise Angina Oldpeak
## 1 0 Normal 172 N 0.0
## 2 0 Normal 156 N 1.0
## 3 0 ST 98 N 0.0
## 4 0 Normal 108 Y 1.5
## 5 0 Normal 122 N 0.0
## 6 0 Normal 170 N 0.0
## ST Slope Heart Disease
## 1 Up 0
## 2 Flat 1
## 3 Up 0
## 4 Flat 1
## 5 Up 0
## 6 Up 0Heart data set contains 918 rows and 12 columns containing age, sex, chest pain type, resting BP, cholesterol, fasting BS, resting ECG, max HR, exercise angina, oldpeak, ST slope, and heart disease.
Column Description:
Age: age of the patient [years]
Sex: sex of the patient [M: Male, F: Female]
ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
RestingBP: resting blood pressure [mm Hg]
Cholesterol: serum cholesterol [mm/dl]
FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes’ criteria]
MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]
ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
Oldpeak: oldpeak = ST [Numeric value measured in depression]
ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
HeartDisease: output class [1: heart disease, 0: Normal]
Exploratory Data Analysis
Check the dimensions (total rows and columns)
print("The dimension of heart data set is:")## [1] "The dimension of heart data set is:"dim(heartData)## [1] 918 12Check data type
str(heartData)## 'data.frame': 918 obs. of 12 variables:
## $ Age : int 40 49 37 48 54 39 45 54 37 48 ...
## $ Sex : chr "M" "F" "M" "F" ...
## $ ChestPainType : chr "ATA" "NAP" "ATA" "ASY" ...
## $ RestingBP : int 140 160 130 138 150 120 130 110 140 120 ...
## $ Cholesterol : int 289 180 283 214 195 339 237 208 207 284 ...
## $ FastingBS : int 0 0 0 0 0 0 0 0 0 0 ...
## $ RestingECG : chr "Normal" "Normal" "ST" "Normal" ...
## $ MaxHR : int 172 156 98 108 122 170 170 142 130 120 ...
## $ ExerciseAngina: chr "N" "N" "N" "Y" ...
## $ Oldpeak : num 0 1 0 1.5 0 0 0 0 1.5 0 ...
## $ ST_Slope : chr "Up" "Flat" "Up" "Flat" ...
## $ HeartDisease : int 0 1 0 1 0 0 0 0 1 0 ...Data Type Description -> int: integers, chr: character, and num: number(decimal)
Handle missing value
sapply(heartData, function(x) sum(is.na(x)))## Age Sex ChestPainType RestingBP Cholesterol
## 0 0 0 0 0
## FastingBS RestingECG MaxHR ExerciseAngina Oldpeak
## 0 0 0 0 0
## ST_Slope HeartDisease
## 0 0sprintf("Total missing value is %d", sum(is.na(heartData)))## [1] "Total missing value is 0"There is no missing value in all columns
Check duplicated rows
sprintf("Total duplicated data is %d", sum(duplicated(heartData)))## [1] "Total duplicated data is 0"Find the information to diagnose the quality of each variable in the data frame
diagnose(heartData)## # A tibble: 12 x 6
## variables types missing_count missing_percent unique_count unique_rate
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 Age integer 0 0 50 0.0545
## 2 Sex charac~ 0 0 2 0.00218
## 3 ChestPainType charac~ 0 0 4 0.00436
## 4 RestingBP integer 0 0 67 0.0730
## 5 Cholesterol integer 0 0 222 0.242
## 6 FastingBS integer 0 0 2 0.00218
## 7 RestingECG charac~ 0 0 3 0.00327
## 8 MaxHR integer 0 0 119 0.130
## 9 ExerciseAngina charac~ 0 0 2 0.00218
## 10 Oldpeak numeric 0 0 53 0.0577
## 11 ST_Slope charac~ 0 0 3 0.00327
## 12 HeartDisease integer 0 0 2 0.00218From the table, we can get information about the variables, data type, missing count which is contain the number of missing value, missing percent which is contain the percentage of missing value, unique count which is number of unique value, and unique rate which shows the ratio of unique counts per number of observations. There are three data types which are integer, character, and numeric. There is no missing count and some of unique count in certain variable.
diagnose_numeric(heartData)## # A tibble: 7 x 10
## variables min Q1 mean median Q3 max zero minus outlier
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int>
## 1 Age 28 47 53.5 54 60 77 0 0 0
## 2 RestingBP 0 120 132. 130 140 200 1 0 28
## 3 Cholesterol 0 173. 199. 223 267 603 172 0 183
## 4 FastingBS 0 0 0.233 0 0 1 704 0 214
## 5 MaxHR 60 120 137. 138 156 202 0 0 2
## 6 Oldpeak -2.6 0 0.887 0.6 1.5 6.2 368 13 16
## 7 HeartDisease 0 0 0.553 1 1 1 410 0 0From the table, we can get information about the variables, min, first quartile, mean, median, third quartile, max, zero, minus, and outliers. We can see that there are outliers which are 28 in resting BP, 183 in cholesterol, 214 in fasting BS, 2 in max HR, and 16 in oldpeak.
plot_outlier(heartData)
From the visualization, we can see that there is a lot of outliers in Cholesterol and Fasting BS. When we analize the outliers in fasting BS, we can conclude that it is not correct as 1 means that the number of fasting BS of the patient is more than 120 mg/dl.
Rename variable in heart disease column
heartData <- heartData %>% mutate(HeartDisease = recode(HeartDisease, '1' = "Heart Disease", '0' = "Normal"))
tail(heartData)## Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR
## 913 57 F ASY 140 241 0 Normal 123
## 914 45 M TA 110 264 0 Normal 132
## 915 68 M ASY 144 193 1 Normal 141
## 916 57 M ASY 130 131 0 Normal 115
## 917 57 F ATA 130 236 0 LVH 174
## 918 38 M NAP 138 175 0 Normal 173
## ExerciseAngina Oldpeak ST_Slope HeartDisease
## 913 Y 0.2 Flat Heart Disease
## 914 N 1.2 Flat Heart Disease
## 915 N 3.4 Flat Heart Disease
## 916 Y 1.2 Flat Heart Disease
## 917 N 0.0 Flat Heart Disease
## 918 N 0.0 Up NormalChange char data type into factor
heartData$Sex <- as.factor(heartData$Sex)
heartData$ChestPainType <- as.factor(heartData$ChestPainType)
heartData$RestingECG <- as.factor(heartData$RestingECG)
heartData$ExerciseAngina <- as.factor(heartData$ExerciseAngina)
heartData$ST_Slope <- as.factor(heartData$ST_Slope)
heartData$HeartDisease <- as.factor(heartData$HeartDisease)Remove Outliers
We have to remove the outliers as it can affect the results of the analysis so that the conclusions are wrong in logistic regression. On the other hand, decision trees are not sensitive to outliers as it do not cause much reduction in residual sum of squares since they are never involved in the split. From the numeric diagnose, we can found that there are five columns that contains outliers, yet only two columns that contains quite a lot of outliers, which are 183 in Cholesterol and 214 in Fasting BS.
outliers <- function(x) {
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper = Q3 + (iqr*1.5)
lower = Q1 - (iqr*1.5)
x > upper | x < lower
}
remove_outliers <- function(df, cols = names(df)) {
for (col in cols) {
df <- df[!outliers(df[[col]]),]
}
df
}
heartData <- remove_outliers(heartData, c('Cholesterol'))
heartData## Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR
## 1 40 M ATA 140 289 0 Normal 172
## 2 49 F NAP 160 180 0 Normal 156
## 3 37 M ATA 130 283 0 ST 98
## 4 48 F ASY 138 214 0 Normal 108
## 5 54 M NAP 150 195 0 Normal 122
## 6 39 M NAP 120 339 0 Normal 170
## 7 45 F ATA 130 237 0 Normal 170
## 8 54 M ATA 110 208 0 Normal 142
## 9 37 M ASY 140 207 0 Normal 130
## 10 48 F ATA 120 284 0 Normal 120
## 11 37 F NAP 130 211 0 Normal 142
## 12 58 M ATA 136 164 0 ST 99
## 13 39 M ATA 120 204 0 Normal 145
## 14 49 M ASY 140 234 0 Normal 140
## 15 42 F NAP 115 211 0 ST 137
## 16 54 F ATA 120 273 0 Normal 150
## 17 38 M ASY 110 196 0 Normal 166
## 18 43 F ATA 120 201 0 Normal 165
## 19 60 M ASY 100 248 0 Normal 125
## 20 36 M ATA 120 267 0 Normal 160
## 21 43 F TA 100 223 0 Normal 142
## 22 44 M ATA 120 184 0 Normal 142
## 23 49 F ATA 124 201 0 Normal 164
## 24 44 M ATA 150 288 0 Normal 150
## 25 40 M NAP 130 215 0 Normal 138
## 26 36 M NAP 130 209 0 Normal 178
## 27 53 M ASY 124 260 0 ST 112
## 28 52 M ATA 120 284 0 Normal 118
## 30 51 M ATA 125 188 0 Normal 145
## 32 56 M NAP 130 167 0 Normal 114
## 33 54 M ASY 125 224 0 Normal 122
## 34 41 M ASY 130 172 0 ST 130
## 35 43 F ATA 150 186 0 Normal 154
## 36 32 M ATA 125 254 0 Normal 155
## 37 65 M ASY 140 306 1 Normal 87
## 38 41 F ATA 110 250 0 ST 142
## 39 48 F ATA 120 177 1 ST 148
## 40 48 F ASY 150 227 0 Normal 130
## 41 54 F ATA 150 230 0 Normal 130
## 42 54 F NAP 130 294 0 ST 100
## 43 35 M ATA 150 264 0 Normal 168
## 44 52 M NAP 140 259 0 ST 170
## 45 43 M ASY 120 175 0 Normal 120
## 46 59 M NAP 130 318 0 Normal 120
## 47 37 M ASY 120 223 0 Normal 168
## 48 50 M ATA 140 216 0 Normal 170
## 49 36 M NAP 112 340 0 Normal 184
## 50 41 M ASY 110 289 0 Normal 170
## 51 50 M ASY 130 233 0 Normal 121
## 52 47 F ASY 120 205 0 Normal 98
## 53 45 M ATA 140 224 1 Normal 122
## 54 41 F ATA 130 245 0 Normal 150
## 55 52 F ASY 130 180 0 Normal 140
## 56 51 F ATA 160 194 0 Normal 170
## 57 31 M ASY 120 270 0 Normal 153
## 58 58 M NAP 130 213 0 ST 140
## 59 54 M ASY 150 365 0 ST 134
## 60 52 M ASY 112 342 0 ST 96
## 61 49 M ATA 100 253 0 Normal 174
## 62 43 F NAP 150 254 0 Normal 175
## 63 45 M ASY 140 224 0 Normal 144
## 64 46 M ASY 120 277 0 Normal 125
## 65 50 F ATA 110 202 0 Normal 145
## 66 37 F ATA 120 260 0 Normal 130
## 67 45 F ASY 132 297 0 Normal 144
## 68 32 M ATA 110 225 0 Normal 184
## 69 52 M ASY 160 246 0 ST 82
## 71 57 M ATA 140 265 0 ST 145
## 72 44 M ATA 130 215 0 Normal 135
## 73 52 M ASY 120 182 0 Normal 150
## 74 44 F ASY 120 218 0 ST 115
## 75 55 M ASY 140 268 0 Normal 128
## 76 46 M NAP 150 163 0 Normal 116
## 78 35 F ASY 140 167 0 Normal 150
## 79 52 M ATA 140 100 0 Normal 138
## 80 49 M ASY 130 206 0 Normal 170
## 81 55 M NAP 110 277 0 Normal 160
## 82 54 M ATA 120 238 0 Normal 154
## 83 63 M ASY 150 223 0 Normal 115
## 84 52 M ATA 160 196 0 Normal 165
## 85 56 M ASY 150 213 1 Normal 125
## 86 66 M ASY 140 139 0 Normal 94
## 87 65 M ASY 170 263 1 Normal 112
## 88 53 F ATA 140 216 0 Normal 142
## 89 43 M TA 120 291 0 ST 155
## 90 55 M ASY 140 229 0 Normal 110
## 91 49 F ATA 110 208 0 Normal 160
## 92 39 M ASY 130 307 0 Normal 140
## 93 52 F ATA 120 210 0 Normal 148
## 94 48 M ASY 160 329 0 Normal 92
## 95 39 F NAP 110 182 0 ST 180
## 96 58 M ASY 130 263 0 Normal 140
## 97 43 M ATA 142 207 0 Normal 138
## 98 39 M NAP 160 147 1 Normal 160
## 99 56 M ASY 120 85 0 Normal 140
## 100 41 M ATA 125 269 0 Normal 144
## 101 65 M ASY 130 275 0 ST 115
## 102 51 M ASY 130 179 0 Normal 100
## 103 40 F ASY 150 392 0 Normal 130
## 105 46 M ASY 118 186 0 Normal 124
## 106 57 M ATA 140 260 1 Normal 140
## 107 48 F ASY 120 254 0 ST 110
## 108 34 M ATA 150 214 0 ST 168
## 109 50 M ASY 140 129 0 Normal 135
## 110 39 M ATA 190 241 0 Normal 106
## 111 59 F ATA 130 188 0 Normal 124
## 112 57 M ASY 150 255 0 Normal 92
## 113 47 M ASY 140 276 1 Normal 125
## 114 38 M ATA 140 297 0 Normal 150
## 115 49 F NAP 130 207 0 ST 135
## 116 33 F ASY 100 246 0 Normal 150
## 117 38 M ASY 120 282 0 Normal 170
## 118 59 F ASY 130 338 1 ST 130
## 119 35 F TA 120 160 0 ST 185
## 120 34 M TA 140 156 0 Normal 180
## 121 47 F NAP 135 248 1 Normal 170
## 122 52 F NAP 125 272 0 Normal 139
## 123 46 M ASY 110 240 0 ST 140
## 124 58 F ATA 180 393 0 Normal 110
## 125 58 M ATA 130 230 0 Normal 150
## 126 54 M ATA 120 246 0 Normal 110
## 127 34 F ATA 130 161 0 Normal 190
## 128 48 F ASY 108 163 0 Normal 175
## 129 54 F ATA 120 230 1 Normal 140
## 130 42 M NAP 120 228 0 Normal 152
## 131 38 M NAP 145 292 0 Normal 130
## 132 46 M ASY 110 202 0 Normal 150
## 133 56 M ASY 170 388 0 ST 122
## 134 56 M ASY 150 230 0 ST 124
## 135 61 F ASY 130 294 0 ST 120
## 136 49 M NAP 115 265 0 Normal 175
## 137 43 F ATA 120 215 0 ST 175
## 138 39 M ATA 120 241 0 ST 146
## 139 54 M ASY 140 166 0 Normal 118
## 140 43 M ASY 150 247 0 Normal 130
## 141 52 M ASY 160 331 0 Normal 94
## 142 50 M ASY 140 341 0 ST 125
## 143 47 M ASY 160 291 0 ST 158
## 144 53 M ASY 140 243 0 Normal 155
## 145 56 F ATA 120 279 0 Normal 150
## 146 39 M ASY 110 273 0 Normal 132
## 147 42 M ATA 120 198 0 Normal 155
## 148 43 F ATA 120 249 0 ST 176
## 149 50 M ATA 120 168 0 Normal 160
## 151 39 M ATA 130 215 0 Normal 120
## 152 48 M ATA 100 159 0 Normal 100
## 153 40 M ATA 130 275 0 Normal 150
## 154 55 M ASY 120 270 0 Normal 140
## 155 41 M ATA 120 291 0 ST 160
## 156 56 M ASY 155 342 1 Normal 150
## 157 38 M ASY 110 190 0 Normal 150
## 158 49 M ASY 140 185 0 Normal 130
## 159 44 M ASY 130 290 0 Normal 100
## 160 54 M ATA 160 195 0 ST 130
## 161 59 M ASY 140 264 1 LVH 119
## 162 49 M ASY 128 212 0 Normal 96
## 163 47 M ATA 160 263 0 Normal 174
## 164 42 M ATA 120 196 0 Normal 150
## 165 52 F ATA 140 225 0 Normal 140
## 166 46 M TA 140 272 1 Normal 175
## 167 50 M ASY 140 231 0 ST 140
## 168 48 M ATA 140 238 0 Normal 118
## 169 58 M ASY 135 222 0 Normal 100
## 170 58 M NAP 140 179 0 Normal 160
## 171 29 M ATA 120 243 0 Normal 160
## 172 40 M NAP 140 235 0 Normal 188
## 173 53 M ATA 140 320 0 Normal 162
## 174 49 M NAP 140 187 0 Normal 172
## 175 52 M ASY 140 266 0 Normal 134
## 176 43 M ASY 140 288 0 Normal 135
## 177 54 M ASY 140 216 0 Normal 105
## 178 59 M ATA 140 287 0 Normal 150
## 179 37 M NAP 130 194 0 Normal 150
## 180 46 F ASY 130 238 0 Normal 90
## 181 52 M ASY 130 225 0 Normal 120
## 182 51 M ATA 130 224 0 Normal 150
## 183 52 M ASY 140 404 0 Normal 124
## 184 46 M ASY 110 238 0 ST 140
## 185 54 F ATA 160 312 0 Normal 130
## 186 58 M NAP 160 211 1 ST 92
## 187 58 M ATA 130 251 0 Normal 110
## 188 41 M ASY 120 237 1 Normal 138
## 189 50 F ASY 120 328 0 Normal 110
## 190 53 M ASY 180 285 0 ST 120
## 191 46 M ASY 180 280 0 ST 120
## 192 50 M ATA 170 209 0 ST 116
## 193 48 M ATA 130 245 0 Normal 160
## 194 45 M NAP 135 192 0 Normal 110
## 195 41 F ATA 125 184 0 Normal 180
## 196 62 F TA 160 193 0 Normal 116
## 197 49 M ASY 120 297 0 Normal 132
## 198 42 M ATA 150 268 0 Normal 136
## 199 53 M ASY 120 246 0 Normal 116
## 200 57 F TA 130 308 0 Normal 98
## 201 47 M TA 110 249 0 Normal 150
## 202 46 M NAP 120 230 0 Normal 150
## 203 42 M NAP 160 147 0 Normal 146
## 204 31 F ATA 100 219 0 ST 150
## 205 56 M ATA 130 184 0 Normal 100
## 206 50 M ASY 150 215 0 Normal 140
## 207 35 M ATA 120 308 0 LVH 180
## 208 35 M ATA 110 257 0 Normal 140
## 209 28 M ATA 130 132 0 LVH 185
## 210 54 M ASY 125 216 0 Normal 140
## 211 48 M ASY 106 263 1 Normal 110
## 212 50 F NAP 140 288 0 Normal 140
## 213 56 M NAP 130 276 0 Normal 128
## 214 56 F NAP 130 219 0 ST 164
## 215 47 M ASY 150 226 0 Normal 98
## 216 30 F TA 170 237 0 ST 170
## 217 39 M ASY 110 280 0 Normal 150
## 218 54 M NAP 120 217 0 Normal 137
## 219 55 M ATA 140 196 0 Normal 150
## 220 29 M ATA 140 263 0 Normal 170
## 221 46 M ASY 130 222 0 Normal 112
## 222 51 F ASY 160 303 0 Normal 150
## 223 48 F NAP 120 195 0 Normal 125
## 224 33 M NAP 120 298 0 Normal 185
## 225 55 M ATA 120 256 1 Normal 137
## 226 50 M ASY 145 264 0 Normal 150
## 227 53 M NAP 120 195 0 Normal 140
## 228 38 M ASY 92 117 0 Normal 134
## 229 41 M ATA 120 295 0 Normal 170
## 230 37 F ASY 130 173 0 ST 184
## 231 37 M ASY 130 315 0 Normal 158
## 232 40 M NAP 130 281 0 Normal 167
## 233 38 F ATA 120 275 0 Normal 129
## 234 41 M ASY 112 250 0 Normal 142
## 235 54 F ATA 140 309 0 ST 140
## 236 39 M ATA 120 200 0 Normal 160
## 237 41 M ASY 120 336 0 Normal 118
## 238 55 M TA 140 295 0 Normal 136
## 239 48 M ASY 160 355 0 Normal 99
## 240 48 M ASY 160 193 0 Normal 102
## 241 55 M ATA 145 326 0 Normal 155
## 242 54 M ASY 200 198 0 Normal 142
## 243 55 M ATA 160 292 1 Normal 143
## 244 43 F ATA 120 266 0 Normal 118
## 245 48 M ASY 160 268 0 Normal 103
## 246 54 M TA 120 171 0 Normal 137
## 247 54 M NAP 120 237 0 Normal 150
## 248 48 M ASY 122 275 1 ST 150
## 249 45 M ASY 130 219 0 ST 130
## 250 49 M ASY 130 341 0 Normal 120
## 252 48 M ASY 120 260 0 Normal 115
## 253 61 M ASY 125 292 0 ST 115
## 254 62 M ATA 140 271 0 Normal 152
## 255 55 M ASY 145 248 0 Normal 96
## 256 53 F NAP 120 274 0 Normal 130
## 257 55 F ATA 130 394 0 LVH 150
## 258 36 M NAP 150 160 0 Normal 172
## 259 51 F NAP 150 200 0 Normal 120
## 260 55 F ATA 122 320 0 Normal 155
## 261 46 M ATA 140 275 0 Normal 165
## 262 54 F ATA 120 221 0 Normal 138
## 263 46 M ASY 120 231 0 Normal 115
## 264 59 M ASY 130 126 0 Normal 125
## 265 47 M NAP 140 193 0 Normal 145
## 266 54 M ATA 160 305 0 Normal 175
## 267 52 M ASY 130 298 0 Normal 110
## 268 34 M ATA 98 220 0 Normal 150
## 269 54 M ASY 130 242 0 Normal 91
## 270 47 F NAP 130 235 0 Normal 145
## 271 45 M ASY 120 225 0 Normal 140
## 272 32 F ATA 105 198 0 Normal 165
## 273 55 M ASY 140 201 0 Normal 130
## 274 55 M NAP 120 220 0 LVH 134
## 275 45 F ATA 180 295 0 Normal 180
## 276 59 M NAP 180 213 0 Normal 100
## 277 51 M NAP 135 160 0 Normal 150
## 278 52 M ASY 170 223 0 Normal 126
## 279 57 F ASY 180 347 0 ST 126
## 280 54 F ATA 130 253 0 ST 155
## 281 60 M NAP 120 246 0 LVH 135
## 282 49 M ASY 150 222 0 Normal 122
## 283 51 F NAP 130 220 0 Normal 160
## 284 55 F ATA 110 344 0 ST 160
## 285 42 M ASY 140 358 0 Normal 170
## 286 51 F NAP 110 190 0 Normal 120
## 287 59 M ASY 140 169 0 Normal 140
## 288 53 M ATA 120 181 0 Normal 132
## 289 48 F ATA 133 308 0 ST 156
## 290 36 M ATA 120 166 0 Normal 180
## 291 48 M NAP 110 211 0 Normal 138
## 292 47 F ATA 140 257 0 Normal 135
## 293 53 M ASY 130 182 0 Normal 148
## 417 63 M ASY 140 260 0 ST 112
## 418 44 M ASY 130 209 0 ST 127
## 419 60 M ASY 132 218 0 ST 140
## 420 55 M ASY 142 228 0 ST 149
## 421 66 M NAP 110 213 1 LVH 99
## 423 65 M ASY 150 236 1 ST 105
## 426 60 M ATA 160 267 1 ST 157
## 427 56 M ATA 126 166 0 ST 140
## 432 62 M ASY 120 220 0 ST 86
## 433 63 M ASY 170 177 0 Normal 84
## 434 46 M ASY 110 236 0 Normal 125
## 444 60 M ASY 130 186 1 ST 140
## 445 56 M ASY 120 100 0 Normal 120
## 446 55 M NAP 136 228 0 ST 124
## 448 77 M ASY 124 171 0 ST 110
## 449 63 M ASY 160 230 1 Normal 105
## 453 60 M ASY 140 281 0 ST 118
## 455 58 M ASY 136 203 1 Normal 123
## 461 57 M ASY 139 277 1 ST 118
## 463 59 M ASY 122 233 0 Normal 117
## 466 42 M NAP 134 240 0 Normal 160
## 469 62 M ASY 152 153 0 ST 97
## 470 56 M ATA 124 224 1 Normal 161
## 474 60 M NAP 141 316 1 ST 122
## 477 51 M ASY 132 218 1 LVH 139
## 479 57 M ASY 130 311 1 ST 148
## 483 67 M TA 142 270 1 Normal 125
## 486 63 M ATA 139 217 1 ST 128
## 487 55 M ATA 110 214 1 ST 180
## 488 57 M ASY 140 214 0 ST 144
## 489 65 M TA 140 252 0 Normal 135
## 490 54 M ASY 136 220 0 Normal 140
## 491 72 M NAP 120 214 0 Normal 102
## 492 75 M ASY 170 203 1 ST 108
## 494 51 M NAP 137 339 0 Normal 127
## 495 60 M ASY 142 216 0 Normal 110
## 496 64 F ASY 142 276 0 Normal 140
## 498 61 M ASY 146 241 0 Normal 148
## 499 67 M ASY 160 384 1 ST 130
## 500 62 M ASY 135 297 0 Normal 130
## 501 65 M ASY 136 248 0 Normal 140
## 502 63 M ASY 130 308 0 Normal 138
## 503 69 M ASY 140 208 0 ST 140
## 504 51 M ASY 132 227 1 ST 138
## 505 62 M ASY 158 210 1 Normal 112
## 506 55 M NAP 136 245 1 ST 131
## 507 75 M ASY 136 225 0 Normal 112
## 508 40 M NAP 106 240 0 Normal 80
## 510 58 M ASY 110 198 0 Normal 110
## 511 60 M ASY 136 195 0 Normal 126
## 512 63 M ASY 160 267 1 ST 88
## 513 35 M NAP 123 161 0 ST 153
## 514 62 M TA 112 258 0 ST 150
## 517 68 M NAP 150 195 1 Normal 132
## 518 65 M ASY 150 235 0 Normal 120
## 520 63 M ASY 96 305 0 ST 121
## 521 64 M ASY 130 223 0 ST 128
## 522 61 M ASY 120 282 0 ST 135
## 523 50 M ASY 144 349 0 LVH 120
## 524 59 M ASY 124 160 0 Normal 117
## 525 55 M ASY 150 160 0 ST 150
## 526 45 M NAP 130 236 0 Normal 144
## 527 65 M ASY 144 312 0 LVH 113
## 528 61 M ATA 139 283 0 Normal 135
## 529 49 M NAP 131 142 0 Normal 127
## 530 72 M ASY 143 211 0 Normal 109
## 531 50 M ASY 133 218 0 Normal 128
## 532 64 M ASY 143 306 1 ST 115
## 533 55 M ASY 116 186 1 ST 102
## 534 63 M ASY 110 252 0 ST 140
## 535 59 M ASY 125 222 0 Normal 135
## 538 74 M ASY 150 258 1 ST 130
## 539 54 M ASY 130 202 1 Normal 112
## 540 57 M ASY 110 197 0 LVH 100
## 541 62 M NAP 138 204 0 ST 122
## 542 76 M NAP 104 113 0 LVH 120
## 543 54 F ASY 138 274 0 Normal 105
## 544 70 M ASY 170 192 0 ST 129
## 545 61 F ATA 140 298 1 Normal 120
## 546 48 M ASY 132 272 0 ST 139
## 547 48 M NAP 132 220 1 ST 162
## 548 61 M TA 142 200 1 ST 100
## 549 66 M ASY 112 261 0 Normal 140
## 550 68 M TA 139 181 1 ST 135
## 551 55 M ASY 172 260 0 Normal 73
## 552 62 M NAP 120 220 0 LVH 86
## 553 71 M NAP 144 221 0 Normal 108
## 554 74 M TA 145 216 1 Normal 116
## 555 53 M NAP 155 175 1 ST 160
## 556 58 M NAP 150 219 0 ST 118
## 557 75 M ASY 160 310 1 Normal 112
## 558 56 M NAP 137 208 1 ST 122
## 559 58 M NAP 137 232 0 ST 124
## 560 64 M ASY 134 273 0 Normal 102
## 561 54 M NAP 133 203 0 ST 137
## 562 54 M ATA 132 182 0 ST 141
## 563 59 M ASY 140 274 0 Normal 154
## 564 55 M ASY 135 204 1 ST 126
## 565 57 M ASY 144 270 1 ST 160
## 566 61 M ASY 141 292 0 ST 115
## 567 41 M ASY 150 171 0 Normal 128
## 568 71 M ASY 130 221 0 ST 115
## 569 38 M ASY 110 289 0 Normal 105
## 570 55 M ASY 158 217 0 Normal 110
## 571 56 M ASY 128 223 0 ST 119
## 572 69 M ASY 140 110 1 Normal 109
## 573 64 M ASY 150 193 0 ST 135
## 574 72 M ASY 160 123 1 LVH 130
## 575 69 M ASY 142 210 1 ST 112
## 576 56 M ASY 137 282 1 Normal 126
## 577 62 M ASY 139 170 0 ST 120
## 578 67 M ASY 146 369 0 Normal 110
## 579 57 M ASY 156 173 0 LVH 119
## 580 69 M ASY 145 289 1 ST 110
## 581 51 M ASY 131 152 1 LVH 130
## 582 48 M ASY 140 208 0 Normal 159
## 583 69 M ASY 122 216 1 LVH 84
## 584 69 M NAP 142 271 0 LVH 126
## 585 64 M ASY 141 244 1 ST 116
## 586 57 M ATA 180 285 1 ST 120
## 587 53 M ASY 124 243 0 Normal 122
## 588 37 M NAP 118 240 0 LVH 165
## 589 67 M ASY 140 219 0 ST 122
## 590 74 M NAP 140 237 1 Normal 94
## 591 63 M ATA 136 165 0 ST 133
## 592 58 M ASY 100 213 0 ST 110
## 593 61 M ASY 190 287 1 LVH 150
## 594 64 M ASY 130 258 1 LVH 130
## 595 58 M ASY 160 256 1 LVH 113
## 596 60 M ASY 130 186 1 LVH 140
## 597 57 M ASY 122 264 0 LVH 100
## 598 55 M NAP 133 185 0 ST 136
## 599 55 M ASY 120 226 0 LVH 127
## 600 56 M ASY 130 203 1 Normal 98
## 601 57 M ASY 130 207 0 ST 96
## 602 61 M NAP 140 284 0 Normal 123
## 603 61 M NAP 120 337 0 Normal 98
## 604 74 M ASY 155 310 0 Normal 112
## 605 68 M NAP 134 254 1 Normal 151
## 606 51 F ASY 114 258 1 LVH 96
## 607 62 M ASY 160 254 1 ST 108
## 608 53 M ASY 144 300 1 ST 128
## 609 62 M ASY 158 170 0 ST 138
## 610 46 M ASY 134 310 0 Normal 126
## 611 54 F ASY 127 333 1 ST 154
## 612 62 M TA 135 139 0 ST 137
## 613 55 M ASY 122 223 1 ST 100
## 614 58 M ASY 140 385 1 LVH 135
## 615 62 M ATA 120 254 0 LVH 93
## 616 70 M ASY 130 322 0 LVH 109
## 618 57 M ATA 124 261 0 Normal 141
## 619 64 M ASY 128 263 0 Normal 105
## 620 74 F ATA 120 269 0 LVH 121
## 621 65 M ASY 120 177 0 Normal 140
## 622 56 M NAP 130 256 1 LVH 142
## 623 59 M ASY 110 239 0 LVH 142
## 624 60 M ASY 140 293 0 LVH 170
## 625 63 F ASY 150 407 0 LVH 154
## 626 59 M ASY 135 234 0 Normal 161
## 627 53 M ASY 142 226 0 LVH 111
## 628 44 M NAP 140 235 0 LVH 180
## 629 61 M TA 134 234 0 Normal 145
## 630 57 F ASY 128 303 0 LVH 159
## 631 71 F ASY 112 149 0 Normal 125
## 632 46 M ASY 140 311 0 Normal 120
## 633 53 M ASY 140 203 1 LVH 155
## 634 64 M TA 110 211 0 LVH 144
## 635 40 M TA 140 199 0 Normal 178
## 636 67 M ASY 120 229 0 LVH 129
## 637 48 M ATA 130 245 0 LVH 180
## 638 43 M ASY 115 303 0 Normal 181
## 639 47 M ASY 112 204 0 Normal 143
## 640 54 F ATA 132 288 1 LVH 159
## 641 48 F NAP 130 275 0 Normal 139
## 642 46 F ASY 138 243 0 LVH 152
## 643 51 F NAP 120 295 0 LVH 157
## 644 58 M NAP 112 230 0 LVH 165
## 645 71 F NAP 110 265 1 LVH 130
## 646 57 M NAP 128 229 0 LVH 150
## 647 66 M ASY 160 228 0 LVH 138
## 648 37 F NAP 120 215 0 Normal 170
## 649 59 M ASY 170 326 0 LVH 140
## 650 50 M ASY 144 200 0 LVH 126
## 651 48 M ASY 130 256 1 LVH 150
## 652 61 M ASY 140 207 0 LVH 138
## 653 59 M TA 160 273 0 LVH 125
## 654 42 M NAP 130 180 0 Normal 150
## 655 48 M ASY 122 222 0 LVH 186
## 656 40 M ASY 152 223 0 Normal 181
## 657 62 F ASY 124 209 0 Normal 163
## 658 44 M NAP 130 233 0 Normal 179
## 659 46 M ATA 101 197 1 Normal 156
## 660 59 M NAP 126 218 1 Normal 134
## 661 58 M NAP 140 211 1 LVH 165
## 662 49 M NAP 118 149 0 LVH 126
## 663 44 M ASY 110 197 0 LVH 177
## 664 66 M ATA 160 246 0 Normal 120
## 665 65 F ASY 150 225 0 LVH 114
## 666 42 M ASY 136 315 0 Normal 125
## 667 52 M ATA 128 205 1 Normal 184
## 669 63 F ATA 140 195 0 Normal 179
## 670 45 F ATA 130 234 0 LVH 175
## 671 41 F ATA 105 198 0 Normal 168
## 672 61 M ASY 138 166 0 LVH 125
## 673 60 F NAP 120 178 1 Normal 96
## 674 59 F ASY 174 249 0 Normal 143
## 675 62 M ATA 120 281 0 LVH 103
## 676 57 M NAP 150 126 1 Normal 173
## 677 51 F ASY 130 305 0 Normal 142
## 678 44 M NAP 120 226 0 Normal 169
## 679 60 F TA 150 240 0 Normal 171
## 680 63 M TA 145 233 1 LVH 150
## 681 57 M ASY 150 276 0 LVH 112
## 682 51 M ASY 140 261 0 LVH 186
## 683 58 F ATA 136 319 1 LVH 152
## 684 44 F NAP 118 242 0 Normal 149
## 685 47 M NAP 108 243 0 Normal 152
## 686 61 M ASY 120 260 0 Normal 140
## 687 57 F ASY 120 354 0 Normal 163
## 688 70 M ATA 156 245 0 LVH 143
## 689 76 F NAP 140 197 0 ST 116
## 690 67 F ASY 106 223 0 Normal 142
## 691 45 M ASY 142 309 0 LVH 147
## 692 45 M ASY 104 208 0 LVH 148
## 693 39 F NAP 94 199 0 Normal 179
## 694 42 F NAP 120 209 0 Normal 173
## 695 56 M ATA 120 236 0 Normal 178
## 696 58 M ASY 146 218 0 Normal 105
## 697 35 M ASY 120 198 0 Normal 130
## 698 58 M ASY 150 270 0 LVH 111
## 699 41 M NAP 130 214 0 LVH 168
## 700 57 M ASY 110 201 0 Normal 126
## 701 42 M TA 148 244 0 LVH 178
## 702 62 M ATA 128 208 1 LVH 140
## 703 59 M TA 178 270 0 LVH 145
## 704 41 F ATA 126 306 0 Normal 163
## 705 50 M ASY 150 243 0 LVH 128
## 706 59 M ATA 140 221 0 Normal 164
## 707 61 F ASY 130 330 0 LVH 169
## 708 54 M ASY 124 266 0 LVH 109
## 709 54 M ASY 110 206 0 LVH 108
## 710 52 M ASY 125 212 0 Normal 168
## 711 47 M ASY 110 275 0 LVH 118
## 712 66 M ASY 120 302 0 LVH 151
## 713 58 M ASY 100 234 0 Normal 156
## 714 64 F NAP 140 313 0 Normal 133
## 715 50 F ATA 120 244 0 Normal 162
## 716 44 F NAP 108 141 0 Normal 175
## 717 67 M ASY 120 237 0 Normal 71
## 718 49 F ASY 130 269 0 Normal 163
## 719 57 M ASY 165 289 1 LVH 124
## 720 63 M ASY 130 254 0 LVH 147
## 721 48 M ASY 124 274 0 LVH 166
## 722 51 M NAP 100 222 0 Normal 143
## 723 60 F ASY 150 258 0 LVH 157
## 724 59 M ASY 140 177 0 Normal 162
## 725 45 F ATA 112 160 0 Normal 138
## 726 55 F ASY 180 327 0 ST 117
## 727 41 M ATA 110 235 0 Normal 153
## 728 60 F ASY 158 305 0 LVH 161
## 729 54 F NAP 135 304 1 Normal 170
## 730 42 M ATA 120 295 0 Normal 162
## 731 49 F ATA 134 271 0 Normal 162
## 732 46 M ASY 120 249 0 LVH 144
## 733 56 F ASY 200 288 1 LVH 133
## 734 66 F TA 150 226 0 Normal 114
## 735 56 M ASY 130 283 1 LVH 103
## 736 49 M NAP 120 188 0 Normal 139
## 737 54 M ASY 122 286 0 LVH 116
## 738 57 M ASY 152 274 0 Normal 88
## 739 65 F NAP 160 360 0 LVH 151
## 740 54 M NAP 125 273 0 LVH 152
## 741 54 F NAP 160 201 0 Normal 163
## 742 62 M ASY 120 267 0 Normal 99
## 743 52 F NAP 136 196 0 LVH 169
## 744 52 M ATA 134 201 0 Normal 158
## 745 60 M ASY 117 230 1 Normal 160
## 746 63 F ASY 108 269 0 Normal 169
## 747 66 M ASY 112 212 0 LVH 132
## 748 42 M ASY 140 226 0 Normal 178
## 749 64 M ASY 120 246 0 LVH 96
## 750 54 M NAP 150 232 0 LVH 165
## 751 46 F NAP 142 177 0 LVH 160
## 752 67 F NAP 152 277 0 Normal 172
## 753 56 M ASY 125 249 1 LVH 144
## 754 34 F ATA 118 210 0 Normal 192
## 755 57 M ASY 132 207 0 Normal 168
## 756 64 M ASY 145 212 0 LVH 132
## 757 59 M ASY 138 271 0 LVH 182
## 758 50 M NAP 140 233 0 Normal 163
## 759 51 M TA 125 213 0 LVH 125
## 760 54 M ATA 192 283 0 LVH 195
## 761 53 M ASY 123 282 0 Normal 95
## 762 52 M ASY 112 230 0 Normal 160
## 763 40 M ASY 110 167 0 LVH 114
## 764 58 M NAP 132 224 0 LVH 173
## 765 41 F NAP 112 268 0 LVH 172
## 766 41 M NAP 112 250 0 Normal 179
## 767 50 F NAP 120 219 0 Normal 158
## 768 54 F NAP 108 267 0 LVH 167
## 769 64 F ASY 130 303 0 Normal 122
## 770 51 F NAP 130 256 0 LVH 149
## 771 46 F ATA 105 204 0 Normal 172
## 772 55 M ASY 140 217 0 Normal 111
## 773 45 M ATA 128 308 0 LVH 170
## 774 56 M TA 120 193 0 LVH 162
## 775 66 F ASY 178 228 1 Normal 165
## 776 38 M TA 120 231 0 Normal 182
## 777 62 F ASY 150 244 0 Normal 154
## 778 55 M ATA 130 262 0 Normal 155
## 779 58 M ASY 128 259 0 LVH 130
## 780 43 M ASY 110 211 0 Normal 161
## 781 64 F ASY 180 325 0 Normal 154
## 782 50 F ASY 110 254 0 LVH 159
## 783 53 M NAP 130 197 1 LVH 152
## 784 45 F ASY 138 236 0 LVH 152
## 785 65 M TA 138 282 1 LVH 174
## 786 69 M TA 160 234 1 LVH 131
## 787 69 M NAP 140 254 0 LVH 146
## 788 67 M ASY 100 299 0 LVH 125
## 789 68 F NAP 120 211 0 LVH 115
## 790 34 M TA 118 182 0 LVH 174
## 791 62 F ASY 138 294 1 Normal 106
## 792 51 M ASY 140 298 0 Normal 122
## 793 46 M NAP 150 231 0 Normal 147
## 794 67 M ASY 125 254 1 Normal 163
## 795 50 M NAP 129 196 0 Normal 163
## 796 42 M NAP 120 240 1 Normal 194
## 798 41 M ASY 110 172 0 LVH 158
## 799 42 F ASY 102 265 0 LVH 122
## 800 53 M NAP 130 246 1 LVH 173
## 801 43 M NAP 130 315 0 Normal 162
## 802 56 M ASY 132 184 0 LVH 105
## 803 52 M ASY 108 233 1 Normal 147
## 804 62 F ASY 140 394 0 LVH 157
## 805 70 M NAP 160 269 0 Normal 112
## 806 54 M ASY 140 239 0 Normal 160
## 807 70 M ASY 145 174 0 Normal 125
## 808 54 M ATA 108 309 0 Normal 156
## 809 35 M ASY 126 282 0 LVH 156
## 810 48 M NAP 124 255 1 Normal 175
## 811 55 F ATA 135 250 0 LVH 161
## 812 58 F ASY 100 248 0 LVH 122
## 813 54 F NAP 110 214 0 Normal 158
## 814 69 F TA 140 239 0 Normal 151
## 815 77 M ASY 125 304 0 LVH 162
## 816 68 M NAP 118 277 0 Normal 151
## 817 58 M ASY 125 300 0 LVH 171
## 818 60 M ASY 125 258 0 LVH 141
## 819 51 M ASY 140 299 0 Normal 173
## 820 55 M ASY 160 289 0 LVH 145
## 821 52 M TA 152 298 1 Normal 178
## 822 60 F NAP 102 318 0 Normal 160
## 823 58 M NAP 105 240 0 LVH 154
## 824 64 M NAP 125 309 0 Normal 131
## 825 37 M NAP 130 250 0 Normal 187
## 826 59 M TA 170 288 0 LVH 159
## 827 51 M NAP 125 245 1 LVH 166
## 828 43 F NAP 122 213 0 Normal 165
## 829 58 M ASY 128 216 0 LVH 131
## 830 29 M ATA 130 204 0 LVH 202
## 831 41 F ATA 130 204 0 LVH 172
## 832 63 F NAP 135 252 0 LVH 172
## 833 51 M NAP 94 227 0 Normal 154
## 834 54 M NAP 120 258 0 LVH 147
## 835 44 M ATA 120 220 0 Normal 170
## 836 54 M ASY 110 239 0 Normal 126
## 837 65 M ASY 135 254 0 LVH 127
## 838 57 M NAP 150 168 0 Normal 174
## 839 63 M ASY 130 330 1 LVH 132
## 840 35 F ASY 138 183 0 Normal 182
## 841 41 M ATA 135 203 0 Normal 132
## 842 62 F NAP 130 263 0 Normal 97
## 843 43 F ASY 132 341 1 LVH 136
## 844 58 F TA 150 283 1 LVH 162
## 845 52 M TA 118 186 0 LVH 190
## 846 61 F ASY 145 307 0 LVH 146
## 847 39 M ASY 118 219 0 Normal 140
## 848 45 M ASY 115 260 0 LVH 185
## 849 52 M ASY 128 255 0 Normal 161
## 850 62 M NAP 130 231 0 Normal 146
## 851 62 F ASY 160 164 0 LVH 145
## 852 53 F ASY 138 234 0 LVH 160
## 853 43 M ASY 120 177 0 LVH 120
## 854 47 M NAP 138 257 0 LVH 156
## 855 52 M ATA 120 325 0 Normal 172
## 856 68 M NAP 180 274 1 LVH 150
## 857 39 M NAP 140 321 0 LVH 182
## 858 53 F ASY 130 264 0 LVH 143
## 859 62 F ASY 140 268 0 LVH 160
## 860 51 F NAP 140 308 0 LVH 142
## 861 60 M ASY 130 253 0 Normal 144
## 862 65 M ASY 110 248 0 LVH 158
## 863 65 F NAP 155 269 0 Normal 148
## 864 60 M NAP 140 185 0 LVH 155
## 865 60 M ASY 145 282 0 LVH 142
## 866 54 M ASY 120 188 0 Normal 113
## 867 44 M ATA 130 219 0 LVH 188
## 868 44 M ASY 112 290 0 LVH 153
## 869 51 M NAP 110 175 0 Normal 123
## 870 59 M NAP 150 212 1 Normal 157
## 871 71 F ATA 160 302 0 Normal 162
## 872 61 M NAP 150 243 1 Normal 137
## 873 55 M ASY 132 353 0 Normal 132
## 874 64 M NAP 140 335 0 Normal 158
## 875 43 M ASY 150 247 0 Normal 171
## 876 58 F NAP 120 340 0 Normal 172
## 877 60 M ASY 130 206 0 LVH 132
## 878 58 M ATA 120 284 0 LVH 160
## 879 49 M ATA 130 266 0 Normal 171
## 880 48 M ATA 110 229 0 Normal 168
## 881 52 M NAP 172 199 1 Normal 162
## 882 44 M ATA 120 263 0 Normal 173
## 883 56 F ATA 140 294 0 LVH 153
## 884 57 M ASY 140 192 0 Normal 148
## 885 67 M ASY 160 286 0 LVH 108
## 886 53 F NAP 128 216 0 LVH 115
## 887 52 M NAP 138 223 0 Normal 169
## 888 43 M ASY 132 247 1 LVH 143
## 889 52 M ASY 128 204 1 Normal 156
## 890 59 M TA 134 204 0 Normal 162
## 891 64 M TA 170 227 0 LVH 155
## 892 66 F NAP 146 278 0 LVH 152
## 893 39 F NAP 138 220 0 Normal 152
## 894 57 M ATA 154 232 0 LVH 164
## 895 58 F ASY 130 197 0 Normal 131
## 896 57 M ASY 110 335 0 Normal 143
## 897 47 M NAP 130 253 0 Normal 179
## 898 55 F ASY 128 205 0 ST 130
## 899 35 M ATA 122 192 0 Normal 174
## 900 61 M ASY 148 203 0 Normal 161
## 901 58 M ASY 114 318 0 ST 140
## 902 58 F ASY 170 225 1 LVH 146
## 903 58 M ATA 125 220 0 Normal 144
## 904 56 M ATA 130 221 0 LVH 163
## 905 56 M ATA 120 240 0 Normal 169
## 906 67 M NAP 152 212 0 LVH 150
## 907 55 F ATA 132 342 0 Normal 166
## 908 44 M ASY 120 169 0 Normal 144
## 909 63 M ASY 140 187 0 LVH 144
## 910 63 F ASY 124 197 0 Normal 136
## 911 41 M ATA 120 157 0 Normal 182
## 912 59 M ASY 164 176 1 LVH 90
## 913 57 F ASY 140 241 0 Normal 123
## 914 45 M TA 110 264 0 Normal 132
## 915 68 M ASY 144 193 1 Normal 141
## 916 57 M ASY 130 131 0 Normal 115
## 917 57 F ATA 130 236 0 LVH 174
## 918 38 M NAP 138 175 0 Normal 173
## ExerciseAngina Oldpeak ST_Slope HeartDisease
## 1 N 0.0 Up Normal
## 2 N 1.0 Flat Heart Disease
## 3 N 0.0 Up Normal
## 4 Y 1.5 Flat Heart Disease
## 5 N 0.0 Up Normal
## 6 N 0.0 Up Normal
## 7 N 0.0 Up Normal
## 8 N 0.0 Up Normal
## 9 Y 1.5 Flat Heart Disease
## 10 N 0.0 Up Normal
## 11 N 0.0 Up Normal
## 12 Y 2.0 Flat Heart Disease
## 13 N 0.0 Up Normal
## 14 Y 1.0 Flat Heart Disease
## 15 N 0.0 Up Normal
## 16 N 1.5 Flat Normal
## 17 N 0.0 Flat Heart Disease
## 18 N 0.0 Up Normal
## 19 N 1.0 Flat Heart Disease
## 20 N 3.0 Flat Heart Disease
## 21 N 0.0 Up Normal
## 22 N 1.0 Flat Normal
## 23 N 0.0 Up Normal
## 24 Y 3.0 Flat Heart Disease
## 25 N 0.0 Up Normal
## 26 N 0.0 Up Normal
## 27 Y 3.0 Flat Normal
## 28 N 0.0 Up Normal
## 30 N 0.0 Up Normal
## 32 N 0.0 Up Normal
## 33 N 2.0 Flat Heart Disease
## 34 N 2.0 Flat Heart Disease
## 35 N 0.0 Up Normal
## 36 N 0.0 Up Normal
## 37 Y 1.5 Flat Heart Disease
## 38 N 0.0 Up Normal
## 39 N 0.0 Up Normal
## 40 Y 1.0 Flat Normal
## 41 N 0.0 Up Normal
## 42 Y 0.0 Flat Heart Disease
## 43 N 0.0 Up Normal
## 44 N 0.0 Up Normal
## 45 Y 1.0 Flat Heart Disease
## 46 Y 1.0 Flat Normal
## 47 N 0.0 Up Normal
## 48 N 0.0 Up Normal
## 49 N 1.0 Flat Normal
## 50 N 0.0 Flat Heart Disease
## 51 Y 2.0 Flat Heart Disease
## 52 Y 2.0 Flat Heart Disease
## 53 N 0.0 Up Normal
## 54 N 0.0 Up Normal
## 55 Y 1.5 Flat Normal
## 56 N 0.0 Up Normal
## 57 Y 1.5 Flat Heart Disease
## 58 N 0.0 Flat Heart Disease
## 59 N 1.0 Up Normal
## 60 Y 1.0 Flat Heart Disease
## 61 N 0.0 Up Normal
## 62 N 0.0 Up Normal
## 63 N 0.0 Up Normal
## 64 Y 1.0 Flat Heart Disease
## 65 N 0.0 Up Normal
## 66 N 0.0 Up Normal
## 67 N 0.0 Up Normal
## 68 N 0.0 Up Normal
## 69 Y 4.0 Flat Heart Disease
## 71 Y 1.0 Flat Heart Disease
## 72 N 0.0 Up Normal
## 73 N 0.0 Flat Heart Disease
## 74 N 0.0 Up Normal
## 75 Y 1.5 Flat Heart Disease
## 76 N 0.0 Up Normal
## 78 N 0.0 Up Normal
## 79 Y 0.0 Up Normal
## 80 N 0.0 Flat Heart Disease
## 81 N 0.0 Up Normal
## 82 N 0.0 Up Normal
## 83 N 0.0 Flat Heart Disease
## 84 N 0.0 Up Normal
## 85 Y 1.0 Flat Heart Disease
## 86 Y 1.0 Flat Heart Disease
## 87 Y 2.0 Flat Heart Disease
## 88 Y 2.0 Flat Normal
## 89 N 0.0 Flat Heart Disease
## 90 Y 0.5 Flat Normal
## 91 N 0.0 Up Normal
## 92 N 0.0 Up Normal
## 93 N 0.0 Up Normal
## 94 Y 1.5 Flat Heart Disease
## 95 N 0.0 Up Normal
## 96 Y 2.0 Flat Heart Disease
## 97 N 0.0 Up Normal
## 98 N 0.0 Up Normal
## 99 N 0.0 Up Normal
## 100 N 0.0 Up Normal
## 101 Y 1.0 Flat Heart Disease
## 102 N 0.0 Up Normal
## 103 N 2.0 Flat Heart Disease
## 105 N 0.0 Flat Heart Disease
## 106 N 0.0 Up Normal
## 107 N 0.0 Up Normal
## 108 N 0.0 Up Normal
## 109 N 0.0 Up Normal
## 110 N 0.0 Up Normal
## 111 N 1.0 Flat Normal
## 112 Y 3.0 Flat Heart Disease
## 113 Y 0.0 Up Normal
## 114 N 0.0 Up Normal
## 115 N 0.0 Up Normal
## 116 Y 1.0 Flat Heart Disease
## 117 N 0.0 Flat Heart Disease
## 118 Y 1.5 Flat Heart Disease
## 119 N 0.0 Up Normal
## 120 N 0.0 Flat Heart Disease
## 121 N 0.0 Flat Heart Disease
## 122 N 0.0 Up Normal
## 123 N 0.0 Up Normal
## 124 Y 1.0 Flat Heart Disease
## 125 N 0.0 Up Normal
## 126 N 0.0 Up Normal
## 127 N 0.0 Up Normal
## 128 N 2.0 Up Normal
## 129 N 0.0 Up Normal
## 130 Y 1.5 Flat Normal
## 131 N 0.0 Up Normal
## 132 Y 0.0 Flat Heart Disease
## 133 Y 2.0 Flat Heart Disease
## 134 Y 1.5 Flat Heart Disease
## 135 Y 1.0 Flat Normal
## 136 N 0.0 Flat Heart Disease
## 137 N 0.0 Up Normal
## 138 N 2.0 Up Normal
## 139 Y 0.0 Flat Heart Disease
## 140 Y 2.0 Flat Heart Disease
## 141 Y 2.5 Flat Heart Disease
## 142 Y 2.5 Flat Heart Disease
## 143 Y 3.0 Flat Heart Disease
## 144 N 0.0 Up Normal
## 145 N 1.0 Flat Heart Disease
## 146 N 0.0 Up Normal
## 147 N 0.0 Up Normal
## 148 N 0.0 Up Normal
## 149 N 0.0 Up Normal
## 151 N 0.0 Up Normal
## 152 N 0.0 Up Normal
## 153 N 0.0 Up Normal
## 154 N 0.0 Up Normal
## 155 N 0.0 Up Normal
## 156 Y 3.0 Flat Heart Disease
## 157 Y 1.0 Flat Heart Disease
## 158 N 0.0 Up Normal
## 159 Y 2.0 Flat Heart Disease
## 160 N 1.0 Up Normal
## 161 Y 0.0 Flat Heart Disease
## 162 Y 0.0 Flat Heart Disease
## 163 N 0.0 Up Normal
## 164 N 0.0 Up Normal
## 165 N 0.0 Up Normal
## 166 N 2.0 Flat Heart Disease
## 167 Y 5.0 Flat Heart Disease
## 168 N 0.0 Up Normal
## 169 N 0.0 Up Normal
## 170 N 0.0 Up Normal
## 171 N 0.0 Up Normal
## 172 N 0.0 Up Normal
## 173 N 0.0 Up Normal
## 174 N 0.0 Up Normal
## 175 Y 2.0 Flat Heart Disease
## 176 Y 2.0 Flat Heart Disease
## 177 N 1.5 Flat Heart Disease
## 178 N 0.0 Up Normal
## 179 N 0.0 Up Normal
## 180 N 0.0 Up Normal
## 181 Y 2.0 Flat Heart Disease
## 182 N 0.0 Up Normal
## 183 Y 2.0 Flat Heart Disease
## 184 Y 1.0 Flat Normal
## 185 N 0.0 Up Normal
## 186 N 0.0 Flat Heart Disease
## 187 N 0.0 Up Normal
## 188 Y 1.0 Flat Heart Disease
## 189 Y 1.0 Flat Normal
## 190 Y 1.5 Flat Heart Disease
## 191 N 0.0 Up Normal
## 192 N 0.0 Up Normal
## 193 N 0.0 Up Normal
## 194 N 0.0 Up Normal
## 195 N 0.0 Up Normal
## 196 N 0.0 Up Normal
## 197 N 1.0 Flat Normal
## 198 N 0.0 Up Normal
## 199 Y 0.0 Flat Heart Disease
## 200 N 1.0 Flat Normal
## 201 N 0.0 Up Normal
## 202 N 0.0 Up Normal
## 203 N 0.0 Up Normal
## 204 N 0.0 Up Normal
## 205 N 0.0 Up Normal
## 206 Y 0.0 Up Normal
## 207 N 0.0 Up Normal
## 208 N 0.0 Flat Heart Disease
## 209 N 0.0 Up Normal
## 210 N 0.0 Flat Heart Disease
## 211 N 0.0 Flat Heart Disease
## 212 Y 0.0 Flat Heart Disease
## 213 Y 1.0 Up Normal
## 214 N 0.0 Up Normal
## 215 Y 1.5 Flat Heart Disease
## 216 N 0.0 Up Normal
## 217 N 0.0 Flat Heart Disease
## 218 N 0.0 Up Normal
## 219 N 0.0 Up Normal
## 220 N 0.0 Up Normal
## 221 N 0.0 Flat Heart Disease
## 222 Y 1.0 Flat Heart Disease
## 223 N 0.0 Up Normal
## 224 N 0.0 Up Normal
## 225 N 0.0 Up Normal
## 226 N 0.0 Flat Heart Disease
## 227 N 0.0 Up Normal
## 228 Y 2.5 Flat Heart Disease
## 229 N 0.0 Up Normal
## 230 N 0.0 Up Normal
## 231 N 0.0 Up Normal
## 232 N 0.0 Up Normal
## 233 N 0.0 Up Normal
## 234 N 0.0 Up Normal
## 235 N 0.0 Up Normal
## 236 Y 1.0 Flat Normal
## 237 Y 3.0 Flat Heart Disease
## 238 N 0.0 Flat Heart Disease
## 239 Y 2.0 Flat Heart Disease
## 240 Y 3.0 Flat Heart Disease
## 241 N 0.0 Up Normal
## 242 Y 2.0 Flat Heart Disease
## 243 Y 2.0 Flat Heart Disease
## 244 N 0.0 Up Normal
## 245 Y 1.0 Flat Heart Disease
## 246 N 2.0 Up Normal
## 247 Y 1.5 Flat Heart Disease
## 248 Y 2.0 Down Heart Disease
## 249 Y 1.0 Flat Heart Disease
## 250 Y 1.0 Flat Heart Disease
## 252 N 2.0 Flat Heart Disease
## 253 Y 0.0 Up Normal
## 254 N 1.0 Up Normal
## 255 Y 2.0 Flat Heart Disease
## 256 N 0.0 Up Normal
## 257 N 0.0 Up Normal
## 258 N 0.0 Up Normal
## 259 N 0.5 Up Normal
## 260 N 0.0 Up Normal
## 261 Y 0.0 Up Normal
## 262 N 1.0 Up Normal
## 263 Y 0.0 Flat Heart Disease
## 264 N 0.0 Flat Heart Disease
## 265 Y 1.0 Flat Heart Disease
## 266 N 0.0 Up Normal
## 267 Y 1.0 Flat Heart Disease
## 268 N 0.0 Up Normal
## 269 Y 1.0 Flat Heart Disease
## 270 N 2.0 Flat Normal
## 271 N 0.0 Up Normal
## 272 N 0.0 Up Normal
## 273 Y 3.0 Flat Heart Disease
## 274 N 0.0 Up Normal
## 275 N 0.0 Up Normal
## 276 N 0.0 Up Normal
## 277 N 2.0 Flat Heart Disease
## 278 Y 1.5 Flat Heart Disease
## 279 Y 0.8 Flat Normal
## 280 N 0.0 Up Normal
## 281 N 0.0 Up Normal
## 282 N 2.0 Flat Heart Disease
## 283 Y 2.0 Up Normal
## 284 N 0.0 Up Normal
## 285 N 0.0 Up Normal
## 286 N 0.0 Up Normal
## 287 N 0.0 Up Normal
## 288 N 0.0 Up Normal
## 289 N 2.0 Up Normal
## 290 N 0.0 Up Normal
## 291 N 0.0 Up Normal
## 292 N 1.0 Up Normal
## 293 N 0.0 Up Normal
## 417 Y 3.0 Flat Heart Disease
## 418 N 0.0 Up Normal
## 419 Y 1.5 Down Heart Disease
## 420 Y 2.5 Up Heart Disease
## 421 Y 1.3 Flat Normal
## 423 Y 0.0 Flat Heart Disease
## 426 N 0.5 Flat Heart Disease
## 427 N 0.0 Up Normal
## 432 N 0.0 Up Normal
## 433 Y 2.5 Down Heart Disease
## 434 Y 2.0 Flat Heart Disease
## 444 Y 0.5 Flat Heart Disease
## 445 Y 1.5 Flat Heart Disease
## 446 Y 1.6 Flat Heart Disease
## 448 Y 2.0 Up Heart Disease
## 449 Y 1.0 Flat Heart Disease
## 453 Y 1.5 Flat Heart Disease
## 455 Y 1.2 Flat Heart Disease
## 461 Y 1.9 Flat Heart Disease
## 463 Y 1.3 Down Heart Disease
## 466 N 0.0 Up Normal
## 469 Y 1.6 Up Heart Disease
## 470 N 2.0 Flat Normal
## 474 Y 1.7 Flat Heart Disease
## 477 N 0.1 Up Normal
## 479 Y 2.0 Flat Heart Disease
## 483 N 2.5 Up Heart Disease
## 486 Y 1.2 Flat Heart Disease
## 487 N 0.4 Up Normal
## 488 Y 2.0 Flat Heart Disease
## 489 N 0.3 Up Normal
## 490 Y 3.0 Flat Heart Disease
## 491 Y 1.0 Flat Heart Disease
## 492 N 0.0 Flat Heart Disease
## 494 Y 1.7 Flat Heart Disease
## 495 Y 2.5 Flat Heart Disease
## 496 Y 1.0 Flat Heart Disease
## 498 Y 3.0 Down Heart Disease
## 499 Y 0.0 Flat Heart Disease
## 500 Y 1.0 Flat Heart Disease
## 501 Y 4.0 Down Heart Disease
## 502 Y 2.0 Flat Heart Disease
## 503 Y 2.0 Flat Heart Disease
## 504 N 0.2 Up Normal
## 505 Y 3.0 Down Heart Disease
## 506 Y 1.2 Flat Heart Disease
## 507 Y 3.0 Flat Heart Disease
## 508 Y 0.0 Up Normal
## 510 N 0.0 Flat Heart Disease
## 511 N 0.3 Up Normal
## 512 Y 2.0 Flat Heart Disease
## 513 N -0.1 Up Normal
## 514 Y 1.3 Flat Heart Disease
## 517 N 0.0 Flat Heart Disease
## 518 Y 1.5 Flat Heart Disease
## 520 Y 1.0 Up Heart Disease
## 521 N 0.5 Flat Normal
## 522 Y 4.0 Down Heart Disease
## 523 Y 1.0 Up Heart Disease
## 524 Y 1.0 Flat Heart Disease
## 525 N 0.0 Up Normal
## 526 N 0.1 Up Normal
## 527 Y 1.7 Flat Heart Disease
## 528 N 0.3 Up Normal
## 529 Y 1.5 Flat Heart Disease
## 530 Y 1.4 Flat Heart Disease
## 531 Y 1.1 Flat Heart Disease
## 532 Y 1.8 Flat Heart Disease
## 533 N 0.0 Flat Heart Disease
## 534 Y 2.0 Flat Heart Disease
## 535 Y 2.5 Down Heart Disease
## 538 Y 4.0 Down Heart Disease
## 539 Y 2.0 Flat Heart Disease
## 540 N 0.0 Up Normal
## 541 Y 1.2 Flat Heart Disease
## 542 N 3.5 Down Heart Disease
## 543 Y 1.5 Flat Heart Disease
## 544 Y 3.0 Down Heart Disease
## 545 Y 0.0 Up Normal
## 546 N 0.2 Up Normal
## 547 N 0.0 Flat Heart Disease
## 548 N 1.5 Down Heart Disease
## 549 N 1.5 Up Heart Disease
## 550 N 0.2 Up Normal
## 551 N 2.0 Flat Heart Disease
## 552 N 0.0 Up Normal
## 553 Y 1.8 Flat Heart Disease
## 554 Y 1.8 Flat Heart Disease
## 555 N 0.3 Up Normal
## 556 Y 0.0 Flat Heart Disease
## 557 Y 2.0 Down Normal
## 558 Y 1.8 Flat Heart Disease
## 559 Y 1.4 Flat Heart Disease
## 560 Y 4.0 Down Heart Disease
## 561 N 0.2 Up Normal
## 562 N 0.1 Up Normal
## 563 Y 2.0 Flat Normal
## 564 Y 1.1 Flat Heart Disease
## 565 Y 2.0 Flat Heart Disease
## 566 Y 1.7 Flat Heart Disease
## 567 Y 1.5 Flat Normal
## 568 Y 0.0 Flat Heart Disease
## 569 Y 1.5 Down Heart Disease
## 570 Y 2.5 Flat Heart Disease
## 571 Y 2.0 Down Heart Disease
## 572 Y 1.5 Flat Heart Disease
## 573 Y 0.5 Flat Heart Disease
## 574 N 1.5 Flat Heart Disease
## 575 Y 1.5 Flat Heart Disease
## 576 Y 1.2 Flat Heart Disease
## 577 Y 3.0 Flat Heart Disease
## 578 Y 1.9 Flat Heart Disease
## 579 Y 3.0 Down Heart Disease
## 580 Y 1.8 Flat Heart Disease
## 581 Y 1.0 Flat Heart Disease
## 582 Y 1.5 Up Heart Disease
## 583 Y 0.0 Flat Heart Disease
## 584 N 0.3 Up Normal
## 585 Y 1.5 Flat Heart Disease
## 586 N 0.8 Flat Heart Disease
## 587 Y 2.0 Flat Heart Disease
## 588 N 1.0 Flat Normal
## 589 Y 2.0 Flat Heart Disease
## 590 N 0.0 Flat Heart Disease
## 591 N 0.2 Up Normal
## 592 N 0.0 Up Normal
## 593 Y 2.0 Down Heart Disease
## 594 N 0.0 Flat Heart Disease
## 595 Y 1.0 Up Heart Disease
## 596 Y 0.5 Flat Heart Disease
## 597 N 0.0 Flat Heart Disease
## 598 N 0.2 Up Normal
## 599 Y 1.7 Down Heart Disease
## 600 N 1.5 Flat Heart Disease
## 601 Y 1.0 Flat Normal
## 602 Y 1.3 Flat Heart Disease
## 603 Y 0.0 Flat Heart Disease
## 604 Y 1.5 Down Heart Disease
## 605 Y 0.0 Up Normal
## 606 N 1.0 Up Normal
## 607 Y 3.0 Flat Heart Disease
## 608 Y 1.5 Flat Heart Disease
## 609 Y 0.0 Flat Heart Disease
## 610 N 0.0 Flat Heart Disease
## 611 N 0.0 Flat Heart Disease
## 612 N 0.2 Up Normal
## 613 N 0.0 Flat Heart Disease
## 614 N 0.3 Up Normal
## 615 Y 0.0 Flat Heart Disease
## 616 N 2.4 Flat Heart Disease
## 618 N 0.3 Up Heart Disease
## 619 Y 0.2 Flat Normal
## 620 Y 0.2 Up Normal
## 621 N 0.4 Up Normal
## 622 Y 0.6 Flat Heart Disease
## 623 Y 1.2 Flat Heart Disease
## 624 N 1.2 Flat Heart Disease
## 625 N 4.0 Flat Heart Disease
## 626 N 0.5 Flat Normal
## 627 Y 0.0 Up Normal
## 628 N 0.0 Up Normal
## 629 N 2.6 Flat Heart Disease
## 630 N 0.0 Up Normal
## 631 N 1.6 Flat Normal
## 632 Y 1.8 Flat Heart Disease
## 633 Y 3.1 Down Heart Disease
## 634 Y 1.8 Flat Normal
## 635 Y 1.4 Up Normal
## 636 Y 2.6 Flat Heart Disease
## 637 N 0.2 Flat Normal
## 638 N 1.2 Flat Normal
## 639 N 0.1 Up Normal
## 640 Y 0.0 Up Normal
## 641 N 0.2 Up Normal
## 642 Y 0.0 Flat Normal
## 643 N 0.6 Up Normal
## 644 N 2.5 Flat Heart Disease
## 645 N 0.0 Up Normal
## 646 N 0.4 Flat Heart Disease
## 647 N 2.3 Up Normal
## 648 N 0.0 Up Normal
## 649 Y 3.4 Down Heart Disease
## 650 Y 0.9 Flat Heart Disease
## 651 Y 0.0 Up Heart Disease
## 652 Y 1.9 Up Heart Disease
## 653 N 0.0 Up Heart Disease
## 654 N 0.0 Up Normal
## 655 N 0.0 Up Normal
## 656 N 0.0 Up Heart Disease
## 657 N 0.0 Up Normal
## 658 Y 0.4 Up Normal
## 659 N 0.0 Up Normal
## 660 N 2.2 Flat Heart Disease
## 661 N 0.0 Up Normal
## 662 N 0.8 Up Heart Disease
## 663 N 0.0 Up Heart Disease
## 664 Y 0.0 Flat Heart Disease
## 665 N 1.0 Flat Heart Disease
## 666 Y 1.8 Flat Heart Disease
## 667 N 0.0 Up Normal
## 669 N 0.0 Up Normal
## 670 N 0.6 Flat Normal
## 671 N 0.0 Up Normal
## 672 Y 3.6 Flat Heart Disease
## 673 N 0.0 Up Normal
## 674 Y 0.0 Flat Heart Disease
## 675 N 1.4 Flat Heart Disease
## 676 N 0.2 Up Normal
## 677 Y 1.2 Flat Heart Disease
## 678 N 0.0 Up Normal
## 679 N 0.9 Up Normal
## 680 N 2.3 Down Normal
## 681 Y 0.6 Flat Heart Disease
## 682 Y 0.0 Up Normal
## 683 N 0.0 Up Heart Disease
## 684 N 0.3 Flat Normal
## 685 N 0.0 Up Heart Disease
## 686 Y 3.6 Flat Heart Disease
## 687 Y 0.6 Up Normal
## 688 N 0.0 Up Normal
## 689 N 1.1 Flat Normal
## 690 N 0.3 Up Normal
## 691 Y 0.0 Flat Heart Disease
## 692 Y 3.0 Flat Normal
## 693 N 0.0 Up Normal
## 694 N 0.0 Flat Normal
## 695 N 0.8 Up Normal
## 696 N 2.0 Flat Heart Disease
## 697 Y 1.6 Flat Heart Disease
## 698 Y 0.8 Up Heart Disease
## 699 N 2.0 Flat Normal
## 700 Y 1.5 Flat Normal
## 701 N 0.8 Up Normal
## 702 N 0.0 Up Normal
## 703 N 4.2 Down Normal
## 704 N 0.0 Up Normal
## 705 N 2.6 Flat Heart Disease
## 706 Y 0.0 Up Normal
## 707 N 0.0 Up Heart Disease
## 708 Y 2.2 Flat Heart Disease
## 709 Y 0.0 Flat Heart Disease
## 710 N 1.0 Up Heart Disease
## 711 Y 1.0 Flat Heart Disease
## 712 N 0.4 Flat Normal
## 713 N 0.1 Up Heart Disease
## 714 N 0.2 Up Normal
## 715 N 1.1 Up Normal
## 716 N 0.6 Flat Normal
## 717 N 1.0 Flat Heart Disease
## 718 N 0.0 Up Normal
## 719 N 1.0 Flat Heart Disease
## 720 N 1.4 Flat Heart Disease
## 721 N 0.5 Flat Heart Disease
## 722 Y 1.2 Flat Normal
## 723 N 2.6 Flat Heart Disease
## 724 Y 0.0 Up Heart Disease
## 725 N 0.0 Flat Normal
## 726 Y 3.4 Flat Heart Disease
## 727 N 0.0 Up Normal
## 728 N 0.0 Up Heart Disease
## 729 N 0.0 Up Normal
## 730 N 0.0 Up Normal
## 731 N 0.0 Flat Normal
## 732 N 0.8 Up Heart Disease
## 733 Y 4.0 Down Heart Disease
## 734 N 2.6 Down Normal
## 735 Y 1.6 Down Heart Disease
## 736 N 2.0 Flat Heart Disease
## 737 Y 3.2 Flat Heart Disease
## 738 Y 1.2 Flat Heart Disease
## 739 N 0.8 Up Normal
## 740 N 0.5 Down Normal
## 741 N 0.0 Up Normal
## 742 Y 1.8 Flat Heart Disease
## 743 N 0.1 Flat Normal
## 744 N 0.8 Up Normal
## 745 Y 1.4 Up Heart Disease
## 746 Y 1.8 Flat Heart Disease
## 747 Y 0.1 Up Heart Disease
## 748 N 0.0 Up Normal
## 749 Y 2.2 Down Heart Disease
## 750 N 1.6 Up Normal
## 751 Y 1.4 Down Normal
## 752 N 0.0 Up Normal
## 753 Y 1.2 Flat Heart Disease
## 754 N 0.7 Up Normal
## 755 Y 0.0 Up Normal
## 756 N 2.0 Flat Heart Disease
## 757 N 0.0 Up Normal
## 758 N 0.6 Flat Heart Disease
## 759 Y 1.4 Up Normal
## 760 N 0.0 Up Heart Disease
## 761 Y 2.0 Flat Heart Disease
## 762 N 0.0 Up Heart Disease
## 763 Y 2.0 Flat Heart Disease
## 764 N 3.2 Up Heart Disease
## 765 Y 0.0 Up Normal
## 766 N 0.0 Up Normal
## 767 N 1.6 Flat Normal
## 768 N 0.0 Up Normal
## 769 N 2.0 Flat Normal
## 770 N 0.5 Up Normal
## 771 N 0.0 Up Normal
## 772 Y 5.6 Down Heart Disease
## 773 N 0.0 Up Normal
## 774 N 1.9 Flat Normal
## 775 Y 1.0 Flat Heart Disease
## 776 Y 3.8 Flat Heart Disease
## 777 Y 1.4 Flat Heart Disease
## 778 N 0.0 Up Normal
## 779 Y 3.0 Flat Heart Disease
## 780 N 0.0 Up Normal
## 781 Y 0.0 Up Normal
## 782 N 0.0 Up Normal
## 783 N 1.2 Down Normal
## 784 Y 0.2 Flat Normal
## 785 N 1.4 Flat Heart Disease
## 786 N 0.1 Flat Normal
## 787 N 2.0 Flat Heart Disease
## 788 Y 0.9 Flat Heart Disease
## 789 N 1.5 Flat Normal
## 790 N 0.0 Up Normal
## 791 N 1.9 Flat Heart Disease
## 792 Y 4.2 Flat Heart Disease
## 793 N 3.6 Flat Heart Disease
## 794 N 0.2 Flat Heart Disease
## 795 N 0.0 Up Normal
## 796 N 0.8 Down Normal
## 798 N 0.0 Up Heart Disease
## 799 N 0.6 Flat Normal
## 800 N 0.0 Up Normal
## 801 N 1.9 Up Normal
## 802 Y 2.1 Flat Heart Disease
## 803 N 0.1 Up Normal
## 804 N 1.2 Flat Normal
## 805 Y 2.9 Flat Heart Disease
## 806 N 1.2 Up Normal
## 807 Y 2.6 Down Heart Disease
## 808 N 0.0 Up Normal
## 809 Y 0.0 Up Heart Disease
## 810 N 0.0 Up Normal
## 811 N 1.4 Flat Normal
## 812 N 1.0 Flat Normal
## 813 N 1.6 Flat Normal
## 814 N 1.8 Up Normal
## 815 Y 0.0 Up Heart Disease
## 816 N 1.0 Up Normal
## 817 N 0.0 Up Heart Disease
## 818 Y 2.8 Flat Heart Disease
## 819 Y 1.6 Up Heart Disease
## 820 Y 0.8 Flat Heart Disease
## 821 N 1.2 Flat Normal
## 822 N 0.0 Up Normal
## 823 Y 0.6 Flat Normal
## 824 Y 1.8 Flat Heart Disease
## 825 N 3.5 Down Normal
## 826 N 0.2 Flat Heart Disease
## 827 N 2.4 Flat Normal
## 828 N 0.2 Flat Normal
## 829 Y 2.2 Flat Heart Disease
## 830 N 0.0 Up Normal
## 831 N 1.4 Up Normal
## 832 N 0.0 Up Normal
## 833 Y 0.0 Up Normal
## 834 N 0.4 Flat Normal
## 835 N 0.0 Up Normal
## 836 Y 2.8 Flat Heart Disease
## 837 N 2.8 Flat Heart Disease
## 838 N 1.6 Up Normal
## 839 Y 1.8 Up Heart Disease
## 840 N 1.4 Up Normal
## 841 N 0.0 Flat Normal
## 842 N 1.2 Flat Heart Disease
## 843 Y 3.0 Flat Heart Disease
## 844 N 1.0 Up Normal
## 845 N 0.0 Flat Normal
## 846 Y 1.0 Flat Heart Disease
## 847 N 1.2 Flat Heart Disease
## 848 N 0.0 Up Normal
## 849 Y 0.0 Up Heart Disease
## 850 N 1.8 Flat Normal
## 851 N 6.2 Down Heart Disease
## 852 N 0.0 Up Normal
## 853 Y 2.5 Flat Heart Disease
## 854 N 0.0 Up Normal
## 855 N 0.2 Up Normal
## 856 Y 1.6 Flat Heart Disease
## 857 N 0.0 Up Normal
## 858 N 0.4 Flat Normal
## 859 N 3.6 Down Heart Disease
## 860 N 1.5 Up Normal
## 861 Y 1.4 Up Heart Disease
## 862 N 0.6 Up Heart Disease
## 863 N 0.8 Up Normal
## 864 N 3.0 Flat Heart Disease
## 865 Y 2.8 Flat Heart Disease
## 866 N 1.4 Flat Heart Disease
## 867 N 0.0 Up Normal
## 868 N 0.0 Up Heart Disease
## 869 N 0.6 Up Normal
## 870 N 1.6 Up Normal
## 871 N 0.4 Up Normal
## 872 Y 1.0 Flat Normal
## 873 Y 1.2 Flat Heart Disease
## 874 N 0.0 Up Heart Disease
## 875 N 1.5 Up Normal
## 876 N 0.0 Up Normal
## 877 Y 2.4 Flat Heart Disease
## 878 N 1.8 Flat Heart Disease
## 879 N 0.6 Up Normal
## 880 N 1.0 Down Heart Disease
## 881 N 0.5 Up Normal
## 882 N 0.0 Up Normal
## 883 N 1.3 Flat Normal
## 884 N 0.4 Flat Normal
## 885 Y 1.5 Flat Heart Disease
## 886 N 0.0 Up Normal
## 887 N 0.0 Up Normal
## 888 Y 0.1 Flat Heart Disease
## 889 Y 1.0 Flat Heart Disease
## 890 N 0.8 Up Heart Disease
## 891 N 0.6 Flat Normal
## 892 N 0.0 Flat Normal
## 893 N 0.0 Flat Normal
## 894 N 0.0 Up Heart Disease
## 895 N 0.6 Flat Normal
## 896 Y 3.0 Flat Heart Disease
## 897 N 0.0 Up Normal
## 898 Y 2.0 Flat Heart Disease
## 899 N 0.0 Up Normal
## 900 N 0.0 Up Heart Disease
## 901 N 4.4 Down Heart Disease
## 902 Y 2.8 Flat Heart Disease
## 903 N 0.4 Flat Normal
## 904 N 0.0 Up Normal
## 905 N 0.0 Down Normal
## 906 N 0.8 Flat Heart Disease
## 907 N 1.2 Up Normal
## 908 Y 2.8 Down Heart Disease
## 909 Y 4.0 Up Heart Disease
## 910 Y 0.0 Flat Heart Disease
## 911 N 0.0 Up Normal
## 912 N 1.0 Flat Heart Disease
## 913 Y 0.2 Flat Heart Disease
## 914 N 1.2 Flat Heart Disease
## 915 N 3.4 Flat Heart Disease
## 916 Y 1.2 Flat Heart Disease
## 917 N 0.0 Flat Heart Disease
## 918 N 0.0 Up NormalFrom the two columns with outliers, we only remove Cholesterol as outliers in fasting blood sugar is refers to a value of 1 which indicates a fasting blood sugar number above 120 mg/dl. Thus, we will not remove the outliers since the outliers in fasting blood sugar is not a value that deviates from the range.
After we remove the outliers the dimensions of the heart data becomes 12 columns and 735 rows.
Bivariate Categorical
Plot the data
sex <- ggplot(heartData, aes(x=HeartDisease, fill=Sex)) +
theme_light() +
geom_bar(position = "dodge")+
labs(title = "Plot of sex", x = "Heart Disease")+
theme(plot.title = element_text(hjust = 0.5))
sex
From the bar chart, we can see that around 140 female and 240 male is normal. On the other hand, around 40 female and 320 male have heart disease. In conclusion, we can see that around 20% of female and 60% of male get heart disease
CPT <- ggplot(heartData, aes(x=HeartDisease, fill=ChestPainType))+
theme_light() +
geom_bar(position = "dodge")+
labs(title = "Plot of Chest Pain Type", x = "Chest Pain Type")+
theme(plot.title = element_text(hjust = 0.5))
CPT
Asymptomatic means a person who experiences a disease without causing any symptoms. Typical angina characteristics which are Substernal chest discomfort with certain quality and duration characteristics, Provoked by exertion or emotionally unstable, and Relieved by rest. Atypical angina have 2 and non angina have 0 or 1 of typical angina characteristics.
From the bar chart, we can see that people who get heart disease have various type of chest pain, which are around 280 people get Asymptomatic, 25 people get Atypical Angina, 40 people get Non-Anginal Pain, and 20 people get Typical Angina. Interestingly, nearly 400 people who do not have heart disease are Asymptomatic, Thus, we can conclude that mostly people with asymptomatic still have a high potential for heart disease.
FBS <- ggplot(heartData, aes(x=FastingBS, fill=HeartDisease))+
theme_light() +
facet_wrap(~HeartDisease)+
geom_bar(position = "dodge")+
labs(title = "Plot of Fasting Blood Sugar", x = "Fasting BS")+
theme(plot.title = element_text(hjust = 0.5))
FBS
A normal fasting blood sugar is around 100 - 120, people with high fasting blood sugar will have higher possibilty to get heart disease. From the bar chart, we can see that around 80 people with heart disease and 40 normal people fasting blood pressure is more than 120.
ECG <- ggplot(heartData, aes(x=HeartDisease, fill=RestingECG))+
geom_bar(position = "dodge")+
theme_light()+
labs(title = "Plot of Resting ECG", x = "Resting ECG")+
theme(plot.title = element_text(hjust = 0.5))
ECG
Resting ECG is electrocardiogram examination performed while the patient is at rest (in a lying position). LVH shows the probability or definite left ventricular hypertrophy by Estes’ criteria and ST means people have ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV).
From the bar chart, we can see that 90 people LVH, 185 people normal, and 70 ST that have heart disease.
EA <- ggplot(heartData, aes(x=HeartDisease, fill=ExerciseAngina))+
theme_light()+
geom_bar(position = "dodge")+
labs(title = "Plot of Exercise Angina", x = "Exercise Angina")+
theme(plot.title = element_text(hjust = 0.5))
EA
Angina is chest pain due to a lack of blood and oxygen supply to the heart which is a symptom of coronary heart disease. From the bar chart, around 235 people who get heart disease experienced angina and 120 other do not experienced it.
STS <- ggplot(heartData, aes(x=HeartDisease, fill=ST_Slope))+
theme_light()+
geom_bar(position = "dodge")+
labs(title = "Plot of ST Slope", x = "ST Slope") +
theme(plot.title = element_text(hjust = 0.5))
STS
ST slope is used for a more accurate ecg examination to diagnose significant coronary heart disease. From the bar chart, we can see that mostly people who get heart disease have a flat ST Slope and mostly normal people have a up ST Slope.
Bivariate Categorical Summary
sex + plot_spacer() + CPT + plot_spacer() + FBS + plot_spacer() + ECG + plot_spacer() + EA + plot_spacer() + STS
Bivariate Numerical
age <- ggplot(heartData, aes(x=Age, fill=HeartDisease))+
theme(panel.background = element_rect(fill = "#F0EBE3"))+
geom_bar(position = "dodge")+
labs(title = "Plot of Age")+
facet_wrap(~HeartDisease)+
theme(plot.title = element_text(hjust = 0.5))
age
From the bar chart, we can see that people around 45 - 70 years old get heart disease, yet we can not conclude it as normal people also around 40 - 65 years old.
RBP <- ggplot(heartData, aes(x=RestingBP, fill=HeartDisease))+
theme(panel.background = element_rect(fill = "#F0EBE3"))+
geom_bar(position = "dodge")+
labs(title = "Plot of Resting Blood Pressure", x = "Resting BP")+
theme(plot.title = element_text(hjust = 0.5))
RBP
ggplot(heartData, aes(x=RestingBP, fill=HeartDisease))+
theme_light()+
geom_density(alpha = .3)+
labs(title = "Plot of Resting Blood Pressure", x = "Resting BP")+
theme(plot.title = element_text(hjust = 0.5))
Blood pressure is a measure to determine how hard the heart pumps blood throughout the body. Normal Resting Blood Pressure is 120 for the systolic number and 80 for the diastolic number. From the bar chart, we can see that the resting blood pressure is around 115 - 140 for both people with heart disease and normal people.
CHOL <- ggplot(heartData, aes(x=Cholesterol, fill=HeartDisease))+
theme(panel.background = element_rect(fill = "#F0EBE3"))+
geom_bar(position = "dodge")+
labs(title = "Plot of Cholesterol")+
theme(plot.title = element_text(hjust = 0.5))
CHOL
ggplot(heartData, aes(x=Cholesterol, fill=HeartDisease))+
theme(panel.background = element_rect(fill = "#F0EBE3"))+
geom_boxplot() +
labs(title = "Plot of Cholesterol")+
theme(plot.title = element_text(hjust = 0.5))
Cholesterol is a fat found in every cell in the body, normally at 200 mg/dl. When cholesterol enters the blood, it can accumulate in the arterial wall and lead to atherosclerosis and heart disease. The arteries then narrow, slowing or blocking blood flow to the muscles. From the bar chart, we can see that mostly people have normal to high cholesterol, yet there are some people both with heart disease and normal have cholesterol levels less than 100 and around 400.
MHR <- ggplot(heartData, aes(x=MaxHR, fill=HeartDisease))+
theme(panel.background = element_rect(fill = "#F0EBE3"))+
geom_bar(position = "dodge")+
labs(title = "Plot of Max Heart Rate", x = "Max HR")+
facet_wrap(~HeartDisease) +
theme(plot.title = element_text(hjust = 0.5))
MHR
Heart rate is the throbbing caused by the heart, which is due to the blood flow through the heart. The maximum heart rate depends on the age of the person. You can find your heart rate by calculate 220 - your old, the result is your maximum heart rate per minutes. From the bar chart, we can see that 120 - 180 for normal people and 110-150 for people with heart disease . Thus, people with heart disease have lower maximum heart rate than normal people.
OP <- ggplot(heartData, aes(x=Oldpeak, fill=HeartDisease))+
theme(panel.background = element_rect(fill = "#F0EBE3"))+
geom_bar(position = "dodge")+
labs(title = "Plot of Oldpeak")+
facet_wrap(~HeartDisease)+
theme(plot.title = element_text(hjust = 0.5))
OP
ggplot(heartData, aes(x=Oldpeak, fill=HeartDisease))+
theme_light()+
geom_density(alpha = .3)+
labs(title = "Plot of Oldpeak")+
theme(plot.title = element_text(hjust = 0.5))
Oldpeak means ST depression caused by exercise relative to rest. People have a risk to get heart disease iff the oldpeak is around 2.5-4.2. From the bar chart, we can see that normal mostly around 0 and people with heart disease is around 1-3 or even more.
Multivariate
plot_correlate(heartData)## Warning: 'plot_correlate' is deprecated.
## Use 'plot.correlate' instead.
## See help("Deprecated")
From the heatmap, we can see the correlation between the variables, the highest correlation is age and peak with 0.26 then age and resting BP with 0.25.
Data Preparation
Split the data into training data (50%) and validation data (50%)
Create random data set from the train data with total columns 50% of the data frame.
trainData <- heartData
sample_size = floor(0.5*nrow(trainData))
picked = sample(seq_len(nrow(trainData)), size = sample_size)
trainingDF = trainData[picked,]
validationDF = trainData[(-picked),]As the total rows of our train data is 735, we split this data frame in half for a total of 367 lines per data frame.
Training Data Frame
trainingDF## Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR
## 697 35 M ASY 120 198 0 Normal 130
## 118 59 F ASY 130 338 1 ST 130
## 511 60 M ASY 136 195 0 Normal 126
## 27 53 M ASY 124 260 0 ST 112
## 808 54 M ATA 108 309 0 Normal 156
## 904 56 M ATA 130 221 0 LVH 163
## 596 60 M ASY 130 186 1 LVH 140
## 721 48 M ASY 124 274 0 LVH 166
## 469 62 M ASY 152 153 0 ST 97
## 284 55 F ATA 110 344 0 ST 160
## 110 39 M ATA 190 241 0 Normal 106
## 863 65 F NAP 155 269 0 Normal 148
## 129 54 F ATA 120 230 1 Normal 140
## 829 58 M ASY 128 216 0 LVH 131
## 2 49 F NAP 160 180 0 Normal 156
## 137 43 F ATA 120 215 0 ST 175
## 618 57 M ATA 124 261 0 Normal 141
## 786 69 M TA 160 234 1 LVH 131
## 200 57 F TA 130 308 0 Normal 98
## 875 43 M ASY 150 247 0 Normal 171
## 815 77 M ASY 125 304 0 LVH 162
## 492 75 M ASY 170 203 1 ST 108
## 549 66 M ASY 112 261 0 Normal 140
## 54 41 F ATA 130 245 0 Normal 150
## 633 53 M ASY 140 203 1 LVH 155
## 147 42 M ATA 120 198 0 Normal 155
## 42 54 F NAP 130 294 0 ST 100
## 6 39 M NAP 120 339 0 Normal 170
## 716 44 F NAP 108 141 0 Normal 175
## 35 43 F ATA 150 186 0 Normal 154
## 172 40 M NAP 140 235 0 Normal 188
## 911 41 M ATA 120 157 0 Normal 182
## 683 58 F ATA 136 319 1 LVH 152
## 532 64 M ASY 143 306 1 ST 115
## 502 63 M ASY 130 308 0 Normal 138
## 574 72 M ASY 160 123 1 LVH 130
## 46 59 M NAP 130 318 0 Normal 120
## 675 62 M ATA 120 281 0 LVH 103
## 542 76 M NAP 104 113 0 LVH 120
## 798 41 M ASY 110 172 0 LVH 158
## 249 45 M ASY 130 219 0 ST 130
## 642 46 F ASY 138 243 0 LVH 152
## 499 67 M ASY 160 384 1 ST 130
## 771 46 F ATA 105 204 0 Normal 172
## 111 59 F ATA 130 188 0 Normal 124
## 769 64 F ASY 130 303 0 Normal 122
## 242 54 M ASY 200 198 0 Normal 142
## 270 47 F NAP 130 235 0 Normal 145
## 620 74 F ATA 120 269 0 LVH 121
## 625 63 F ASY 150 407 0 LVH 154
## 25 40 M NAP 130 215 0 Normal 138
## 240 48 M ASY 160 193 0 Normal 102
## 212 50 F NAP 140 288 0 Normal 140
## 434 46 M ASY 110 236 0 Normal 125
## 777 62 F ASY 150 244 0 Normal 154
## 197 49 M ASY 120 297 0 Normal 132
## 233 38 F ATA 120 275 0 Normal 129
## 680 63 M TA 145 233 1 LVH 150
## 92 39 M ASY 130 307 0 Normal 140
## 580 69 M ASY 145 289 1 ST 110
## 269 54 M ASY 130 242 0 Normal 91
## 917 57 F ATA 130 236 0 LVH 174
## 840 35 F ASY 138 183 0 Normal 182
## 206 50 M ASY 150 215 0 Normal 140
## 630 57 F ASY 128 303 0 LVH 159
## 671 41 F ATA 105 198 0 Normal 168
## 853 43 M ASY 120 177 0 LVH 120
## 160 54 M ATA 160 195 0 ST 130
## 892 66 F NAP 146 278 0 LVH 152
## 45 43 M ASY 120 175 0 Normal 120
## 746 63 F ASY 108 269 0 Normal 169
## 243 55 M ATA 160 292 1 Normal 143
## 849 52 M ASY 128 255 0 Normal 161
## 835 44 M ATA 120 220 0 Normal 170
## 782 50 F ASY 110 254 0 LVH 159
## 608 53 M ASY 144 300 1 ST 128
## 496 64 F ASY 142 276 0 Normal 140
## 247 54 M NAP 120 237 0 Normal 150
## 749 64 M ASY 120 246 0 LVH 96
## 67 45 F ASY 132 297 0 Normal 144
## 764 58 M NAP 132 224 0 LVH 173
## 757 59 M ASY 138 271 0 LVH 182
## 287 59 M ASY 140 169 0 Normal 140
## 559 58 M NAP 137 232 0 ST 124
## 563 59 M ASY 140 274 0 Normal 154
## 719 57 M ASY 165 289 1 LVH 124
## 830 29 M ATA 130 204 0 LVH 202
## 564 55 M ASY 135 204 1 ST 126
## 865 60 M ASY 145 282 0 LVH 142
## 444 60 M ASY 130 186 1 ST 140
## 872 61 M NAP 150 243 1 Normal 137
## 525 55 M ASY 150 160 0 ST 150
## 547 48 M NAP 132 220 1 ST 162
## 662 49 M NAP 118 149 0 LVH 126
## 87 65 M ASY 170 263 1 Normal 112
## 702 62 M ATA 128 208 1 LVH 140
## 58 58 M NAP 130 213 0 ST 140
## 655 48 M ASY 122 222 0 LVH 186
## 743 52 F NAP 136 196 0 LVH 169
## 561 54 M NAP 133 203 0 ST 137
## 635 40 M TA 140 199 0 Normal 178
## 582 48 M ASY 140 208 0 Normal 159
## 727 41 M ATA 110 235 0 Normal 153
## 816 68 M NAP 118 277 0 Normal 151
## 669 63 F ATA 140 195 0 Normal 179
## 660 59 M NAP 126 218 1 Normal 134
## 902 58 F ASY 170 225 1 LVH 146
## 560 64 M ASY 134 273 0 Normal 102
## 760 54 M ATA 192 283 0 LVH 195
## 693 39 F NAP 94 199 0 Normal 179
## 734 66 F TA 150 226 0 Normal 114
## 138 39 M ATA 120 241 0 ST 146
## 68 32 M ATA 110 225 0 Normal 184
## 847 39 M ASY 118 219 0 Normal 140
## 221 46 M ASY 130 222 0 Normal 112
## 736 49 M NAP 120 188 0 Normal 139
## 466 42 M NAP 134 240 0 Normal 160
## 47 37 M ASY 120 223 0 Normal 168
## 694 42 F NAP 120 209 0 Normal 173
## 22 44 M ATA 120 184 0 Normal 142
## 866 54 M ASY 120 188 0 Normal 113
## 155 41 M ATA 120 291 0 ST 160
## 43 35 M ATA 150 264 0 Normal 168
## 652 61 M ASY 140 207 0 LVH 138
## 237 41 M ASY 120 336 0 Normal 118
## 159 44 M ASY 130 290 0 Normal 100
## 897 47 M NAP 130 253 0 Normal 179
## 133 56 M ASY 170 388 0 ST 122
## 573 64 M ASY 150 193 0 ST 135
## 755 57 M ASY 132 207 0 Normal 168
## 161 59 M ASY 140 264 1 LVH 119
## 448 77 M ASY 124 171 0 ST 110
## 286 51 F NAP 110 190 0 Normal 120
## 62 43 F NAP 150 254 0 Normal 175
## 218 54 M NAP 120 217 0 Normal 137
## 545 61 F ATA 140 298 1 Normal 120
## 528 61 M ATA 139 283 0 Normal 135
## 725 45 F ATA 112 160 0 Normal 138
## 583 69 M ASY 122 216 1 LVH 84
## 131 38 M NAP 145 292 0 Normal 130
## 17 38 M ASY 110 196 0 Normal 166
## 595 58 M ASY 160 256 1 LVH 113
## 843 43 F ASY 132 341 1 LVH 136
## 624 60 M ASY 140 293 0 LVH 170
## 818 60 M ASY 125 258 0 LVH 141
## 96 58 M ASY 130 263 0 Normal 140
## 116 33 F ASY 100 246 0 Normal 150
## 845 52 M TA 118 186 0 LVH 190
## 24 44 M ATA 150 288 0 Normal 150
## 593 61 M ASY 190 287 1 LVH 150
## 522 61 M ASY 120 282 0 ST 135
## 289 48 F ATA 133 308 0 ST 156
## 226 50 M ASY 145 264 0 Normal 150
## 101 65 M ASY 130 275 0 ST 115
## 867 44 M ATA 130 219 0 LVH 188
## 176 43 M ASY 140 288 0 Normal 135
## 120 34 M TA 140 156 0 Normal 180
## 707 61 F ASY 130 330 0 LVH 169
## 520 63 M ASY 96 305 0 ST 121
## 419 60 M ASY 132 218 0 ST 140
## 787 69 M NAP 140 254 0 LVH 146
## 556 58 M NAP 150 219 0 ST 118
## 445 56 M ASY 120 100 0 Normal 120
## 122 52 F NAP 125 272 0 Normal 139
## 639 47 M ASY 112 204 0 Normal 143
## 281 60 M NAP 120 246 0 LVH 135
## 19 60 M ASY 100 248 0 Normal 125
## 803 52 M ASY 108 233 1 Normal 147
## 578 67 M ASY 146 369 0 Normal 110
## 236 39 M ATA 120 200 0 Normal 160
## 641 48 F NAP 130 275 0 Normal 139
## 254 62 M ATA 140 271 0 Normal 152
## 914 45 M TA 110 264 0 Normal 132
## 848 45 M ASY 115 260 0 LVH 185
## 813 54 F NAP 110 214 0 Normal 158
## 722 51 M NAP 100 222 0 Normal 143
## 5 54 M NAP 150 195 0 Normal 122
## 718 49 F ASY 130 269 0 Normal 163
## 531 50 M ASY 133 218 0 Normal 128
## 517 68 M NAP 150 195 1 Normal 132
## 672 61 M ASY 138 166 0 LVH 125
## 73 52 M ASY 120 182 0 Normal 150
## 710 52 M ASY 125 212 0 Normal 168
## 791 62 F ASY 138 294 1 Normal 106
## 181 52 M ASY 130 225 0 Normal 120
## 871 71 F ATA 160 302 0 Normal 162
## 139 54 M ASY 140 166 0 Normal 118
## 800 53 M NAP 130 246 1 LVH 173
## 524 59 M ASY 124 160 0 Normal 117
## 229 41 M ATA 120 295 0 Normal 170
## 860 51 F NAP 140 308 0 LVH 142
## 879 49 M ATA 130 266 0 Normal 171
## 822 60 F NAP 102 318 0 Normal 160
## 117 38 M ASY 120 282 0 Normal 170
## 569 38 M ASY 110 289 0 Normal 105
## 799 42 F ASY 102 265 0 LVH 122
## 859 62 F ASY 140 268 0 LVH 160
## 163 47 M ATA 160 263 0 Normal 174
## 7 45 F ATA 130 237 0 Normal 170
## 103 40 F ASY 150 392 0 Normal 130
## 234 41 M ASY 112 250 0 Normal 142
## 884 57 M ASY 140 192 0 Normal 148
## 134 56 M ASY 150 230 0 ST 124
## 82 54 M ATA 120 238 0 Normal 154
## 56 51 F ATA 160 194 0 Normal 170
## 577 62 M ASY 139 170 0 ST 120
## 546 48 M ASY 132 272 0 ST 139
## 657 62 F ASY 124 209 0 Normal 163
## 167 50 M ASY 140 231 0 ST 140
## 585 64 M ASY 141 244 1 ST 116
## 658 44 M NAP 130 233 0 Normal 179
## 189 50 F ASY 120 328 0 Normal 110
## 576 56 M ASY 137 282 1 Normal 126
## 883 56 F ATA 140 294 0 LVH 153
## 169 58 M ASY 135 222 0 Normal 100
## 691 45 M ASY 142 309 0 LVH 147
## 836 54 M ASY 110 239 0 Normal 126
## 273 55 M ASY 140 201 0 Normal 130
## 288 53 M ATA 120 181 0 Normal 132
## 684 44 F NAP 118 242 0 Normal 149
## 60 52 M ASY 112 342 0 ST 96
## 102 51 M ASY 130 179 0 Normal 100
## 193 48 M ATA 130 245 0 Normal 160
## 20 36 M ATA 120 267 0 Normal 160
## 819 51 M ASY 140 299 0 Normal 173
## 606 51 F ASY 114 258 1 LVH 96
## 589 67 M ASY 140 219 0 ST 122
## 231 37 M ASY 130 315 0 Normal 158
## 205 56 M ATA 130 184 0 Normal 100
## 698 58 M ASY 150 270 0 LVH 111
## 821 52 M TA 152 298 1 Normal 178
## 486 63 M ATA 139 217 1 ST 128
## 184 46 M ASY 110 238 0 ST 140
## 258 36 M NAP 150 160 0 Normal 172
## 215 47 M ASY 150 226 0 Normal 98
## 165 52 F ATA 140 225 0 Normal 140
## 601 57 M ASY 130 207 0 ST 96
## 38 41 F ATA 110 250 0 ST 142
## 13 39 M ATA 120 204 0 Normal 145
## 619 64 M ASY 128 263 0 Normal 105
## 121 47 F NAP 135 248 1 Normal 170
## 513 35 M NAP 123 161 0 ST 153
## 453 60 M ASY 140 281 0 ST 118
## 651 48 M ASY 130 256 1 LVH 150
## 203 42 M NAP 160 147 0 Normal 146
## 792 51 M ASY 140 298 0 Normal 122
## 225 55 M ATA 120 256 1 Normal 137
## 15 42 F NAP 115 211 0 ST 137
## 88 53 F ATA 140 216 0 Normal 142
## 235 54 F ATA 140 309 0 ST 140
## 761 53 M ASY 123 282 0 Normal 95
## 723 60 F ASY 150 258 0 LVH 157
## 8 54 M ATA 110 208 0 Normal 142
## 795 50 M NAP 129 196 0 Normal 163
## 858 53 F ASY 130 264 0 LVH 143
## 501 65 M ASY 136 248 0 Normal 140
## 69 52 M ASY 160 246 0 ST 82
## 823 58 M NAP 105 240 0 LVH 154
## 538 74 M ASY 150 258 1 ST 130
## 123 46 M ASY 110 240 0 ST 140
## 503 69 M ASY 140 208 0 ST 140
## 157 38 M ASY 110 190 0 Normal 150
## 127 34 F ATA 130 161 0 Normal 190
## 55 52 F ASY 130 180 0 Normal 140
## 40 48 F ASY 150 227 0 Normal 130
## 852 53 F ASY 138 234 0 LVH 160
## 271 45 M ASY 120 225 0 Normal 140
## 703 59 M TA 178 270 0 LVH 145
## 784 45 F ASY 138 236 0 LVH 152
## 108 34 M ATA 150 214 0 ST 168
## 223 48 F NAP 120 195 0 Normal 125
## 851 62 F ASY 160 164 0 LVH 145
## 874 64 M NAP 140 335 0 Normal 158
## 894 57 M ATA 154 232 0 LVH 164
## 4 48 F ASY 138 214 0 Normal 108
## 591 63 M ATA 136 165 0 ST 133
## 880 48 M ATA 110 229 0 Normal 168
## 833 51 M NAP 94 227 0 Normal 154
## 690 67 F ASY 106 223 0 Normal 142
## 740 54 M NAP 125 273 0 LVH 152
## 676 57 M NAP 150 126 1 Normal 173
## 66 37 F ATA 120 260 0 Normal 130
## 778 55 M ATA 130 262 0 Normal 155
## 491 72 M NAP 120 214 0 Normal 102
## 216 30 F TA 170 237 0 ST 170
## 265 47 M NAP 140 193 0 Normal 145
## 162 49 M ASY 128 212 0 Normal 96
## 731 49 F ATA 134 271 0 Normal 162
## 862 65 M ASY 110 248 0 LVH 158
## 204 31 F ATA 100 219 0 ST 150
## 768 54 F NAP 108 267 0 LVH 167
## 449 63 M ASY 160 230 1 Normal 105
## 80 49 M ASY 130 206 0 Normal 170
## 844 58 F TA 150 283 1 LVH 162
## 278 52 M ASY 170 223 0 Normal 126
## 114 38 M ATA 140 297 0 Normal 150
## 202 46 M NAP 120 230 0 Normal 150
## 730 42 M ATA 120 295 0 Normal 162
## 647 66 M ASY 160 228 0 LVH 138
## 455 58 M ASY 136 203 1 Normal 123
## 885 67 M ASY 160 286 0 LVH 108
## 562 54 M ATA 132 182 0 ST 141
## 701 42 M TA 148 244 0 LVH 178
## 696 58 M ASY 146 218 0 Normal 105
## 245 48 M ASY 160 268 0 Normal 103
## 741 54 F NAP 160 201 0 Normal 163
## 76 46 M NAP 150 163 0 Normal 116
## 65 50 F ATA 110 202 0 Normal 145
## 141 52 M ASY 160 331 0 Normal 94
## 417 63 M ASY 140 260 0 ST 112
## 136 49 M NAP 115 265 0 Normal 175
## 527 65 M ASY 144 312 0 LVH 113
## 144 53 M ASY 140 243 0 Normal 155
## 230 37 F ASY 130 173 0 ST 184
## 889 52 M ASY 128 204 1 Normal 156
## 85 56 M ASY 150 213 1 Normal 125
## 724 59 M ASY 140 177 0 Normal 162
## 483 67 M TA 142 270 1 Normal 125
## 277 51 M NAP 135 160 0 Normal 150
## 276 59 M NAP 180 213 0 Normal 100
## 714 64 F NAP 140 313 0 Normal 133
## 916 57 M ASY 130 131 0 Normal 115
## 809 35 M ASY 126 282 0 LVH 156
## 678 44 M NAP 120 226 0 Normal 169
## 255 55 M ASY 145 248 0 Normal 96
## 514 62 M TA 112 258 0 ST 150
## 246 54 M TA 120 171 0 Normal 137
## 631 71 F ASY 112 149 0 Normal 125
## 841 41 M ATA 135 203 0 Normal 132
## 807 70 M ASY 145 174 0 Normal 125
## 895 58 F ASY 130 197 0 Normal 131
## 682 51 M ASY 140 261 0 LVH 186
## 646 57 M NAP 128 229 0 LVH 150
## 543 54 F ASY 138 274 0 Normal 105
## 470 56 M ATA 124 224 1 Normal 161
## 611 54 F ASY 127 333 1 ST 154
## 599 55 M ASY 120 226 0 LVH 127
## 290 36 M ATA 120 166 0 Normal 180
## 279 57 F ASY 180 347 0 ST 126
## 219 55 M ATA 140 196 0 Normal 150
## 250 49 M ASY 130 341 0 Normal 120
## 98 39 M NAP 160 147 1 Normal 160
## 37 65 M ASY 140 306 1 Normal 87
## 763 40 M ASY 110 167 0 LVH 114
## 700 57 M ASY 110 201 0 Normal 126
## 873 55 M ASY 132 353 0 Normal 132
## 191 46 M ASY 180 280 0 ST 120
## 824 64 M NAP 125 309 0 Normal 131
## 754 34 F ATA 118 210 0 Normal 192
## 626 59 M ASY 135 234 0 Normal 161
## 637 48 M ATA 130 245 0 LVH 180
## 23 49 F ATA 124 201 0 Normal 164
## 112 57 M ASY 150 255 0 Normal 92
## 882 44 M ATA 120 263 0 Normal 173
## 780 43 M ASY 110 211 0 Normal 161
## 850 62 M NAP 130 231 0 Normal 146
## 664 66 M ATA 160 246 0 Normal 120
## 864 60 M NAP 140 185 0 LVH 155
## 869 51 M NAP 110 175 0 Normal 123
## 539 54 M ASY 130 202 1 Normal 112
## 687 57 F ASY 120 354 0 Normal 163
## 262 54 F ATA 120 221 0 Normal 138
## 179 37 M NAP 130 194 0 Normal 150
## 109 50 M ASY 140 129 0 Normal 135
## 238 55 M TA 140 295 0 Normal 136
## 767 50 F NAP 120 219 0 Normal 158
## 856 68 M NAP 180 274 1 LVH 150
## ExerciseAngina Oldpeak ST_Slope HeartDisease
## 697 Y 1.6 Flat Heart Disease
## 118 Y 1.5 Flat Heart Disease
## 511 N 0.3 Up Normal
## 27 Y 3.0 Flat Normal
## 808 N 0.0 Up Normal
## 904 N 0.0 Up Normal
## 596 Y 0.5 Flat Heart Disease
## 721 N 0.5 Flat Heart Disease
## 469 Y 1.6 Up Heart Disease
## 284 N 0.0 Up Normal
## 110 N 0.0 Up Normal
## 863 N 0.8 Up Normal
## 129 N 0.0 Up Normal
## 829 Y 2.2 Flat Heart Disease
## 2 N 1.0 Flat Heart Disease
## 137 N 0.0 Up Normal
## 618 N 0.3 Up Heart Disease
## 786 N 0.1 Flat Normal
## 200 N 1.0 Flat Normal
## 875 N 1.5 Up Normal
## 815 Y 0.0 Up Heart Disease
## 492 N 0.0 Flat Heart Disease
## 549 N 1.5 Up Heart Disease
## 54 N 0.0 Up Normal
## 633 Y 3.1 Down Heart Disease
## 147 N 0.0 Up Normal
## 42 Y 0.0 Flat Heart Disease
## 6 N 0.0 Up Normal
## 716 N 0.6 Flat Normal
## 35 N 0.0 Up Normal
## 172 N 0.0 Up Normal
## 911 N 0.0 Up Normal
## 683 N 0.0 Up Heart Disease
## 532 Y 1.8 Flat Heart Disease
## 502 Y 2.0 Flat Heart Disease
## 574 N 1.5 Flat Heart Disease
## 46 Y 1.0 Flat Normal
## 675 N 1.4 Flat Heart Disease
## 542 N 3.5 Down Heart Disease
## 798 N 0.0 Up Heart Disease
## 249 Y 1.0 Flat Heart Disease
## 642 Y 0.0 Flat Normal
## 499 Y 0.0 Flat Heart Disease
## 771 N 0.0 Up Normal
## 111 N 1.0 Flat Normal
## 769 N 2.0 Flat Normal
## 242 Y 2.0 Flat Heart Disease
## 270 N 2.0 Flat Normal
## 620 Y 0.2 Up Normal
## 625 N 4.0 Flat Heart Disease
## 25 N 0.0 Up Normal
## 240 Y 3.0 Flat Heart Disease
## 212 Y 0.0 Flat Heart Disease
## 434 Y 2.0 Flat Heart Disease
## 777 Y 1.4 Flat Heart Disease
## 197 N 1.0 Flat Normal
## 233 N 0.0 Up Normal
## 680 N 2.3 Down Normal
## 92 N 0.0 Up Normal
## 580 Y 1.8 Flat Heart Disease
## 269 Y 1.0 Flat Heart Disease
## 917 N 0.0 Flat Heart Disease
## 840 N 1.4 Up Normal
## 206 Y 0.0 Up Normal
## 630 N 0.0 Up Normal
## 671 N 0.0 Up Normal
## 853 Y 2.5 Flat Heart Disease
## 160 N 1.0 Up Normal
## 892 N 0.0 Flat Normal
## 45 Y 1.0 Flat Heart Disease
## 746 Y 1.8 Flat Heart Disease
## 243 Y 2.0 Flat Heart Disease
## 849 Y 0.0 Up Heart Disease
## 835 N 0.0 Up Normal
## 782 N 0.0 Up Normal
## 608 Y 1.5 Flat Heart Disease
## 496 Y 1.0 Flat Heart Disease
## 247 Y 1.5 Flat Heart Disease
## 749 Y 2.2 Down Heart Disease
## 67 N 0.0 Up Normal
## 764 N 3.2 Up Heart Disease
## 757 N 0.0 Up Normal
## 287 N 0.0 Up Normal
## 559 Y 1.4 Flat Heart Disease
## 563 Y 2.0 Flat Normal
## 719 N 1.0 Flat Heart Disease
## 830 N 0.0 Up Normal
## 564 Y 1.1 Flat Heart Disease
## 865 Y 2.8 Flat Heart Disease
## 444 Y 0.5 Flat Heart Disease
## 872 Y 1.0 Flat Normal
## 525 N 0.0 Up Normal
## 547 N 0.0 Flat Heart Disease
## 662 N 0.8 Up Heart Disease
## 87 Y 2.0 Flat Heart Disease
## 702 N 0.0 Up Normal
## 58 N 0.0 Flat Heart Disease
## 655 N 0.0 Up Normal
## 743 N 0.1 Flat Normal
## 561 N 0.2 Up Normal
## 635 Y 1.4 Up Normal
## 582 Y 1.5 Up Heart Disease
## 727 N 0.0 Up Normal
## 816 N 1.0 Up Normal
## 669 N 0.0 Up Normal
## 660 N 2.2 Flat Heart Disease
## 902 Y 2.8 Flat Heart Disease
## 560 Y 4.0 Down Heart Disease
## 760 N 0.0 Up Heart Disease
## 693 N 0.0 Up Normal
## 734 N 2.6 Down Normal
## 138 N 2.0 Up Normal
## 68 N 0.0 Up Normal
## 847 N 1.2 Flat Heart Disease
## 221 N 0.0 Flat Heart Disease
## 736 N 2.0 Flat Heart Disease
## 466 N 0.0 Up Normal
## 47 N 0.0 Up Normal
## 694 N 0.0 Flat Normal
## 22 N 1.0 Flat Normal
## 866 N 1.4 Flat Heart Disease
## 155 N 0.0 Up Normal
## 43 N 0.0 Up Normal
## 652 Y 1.9 Up Heart Disease
## 237 Y 3.0 Flat Heart Disease
## 159 Y 2.0 Flat Heart Disease
## 897 N 0.0 Up Normal
## 133 Y 2.0 Flat Heart Disease
## 573 Y 0.5 Flat Heart Disease
## 755 Y 0.0 Up Normal
## 161 Y 0.0 Flat Heart Disease
## 448 Y 2.0 Up Heart Disease
## 286 N 0.0 Up Normal
## 62 N 0.0 Up Normal
## 218 N 0.0 Up Normal
## 545 Y 0.0 Up Normal
## 528 N 0.3 Up Normal
## 725 N 0.0 Flat Normal
## 583 Y 0.0 Flat Heart Disease
## 131 N 0.0 Up Normal
## 17 N 0.0 Flat Heart Disease
## 595 Y 1.0 Up Heart Disease
## 843 Y 3.0 Flat Heart Disease
## 624 N 1.2 Flat Heart Disease
## 818 Y 2.8 Flat Heart Disease
## 96 Y 2.0 Flat Heart Disease
## 116 Y 1.0 Flat Heart Disease
## 845 N 0.0 Flat Normal
## 24 Y 3.0 Flat Heart Disease
## 593 Y 2.0 Down Heart Disease
## 522 Y 4.0 Down Heart Disease
## 289 N 2.0 Up Normal
## 226 N 0.0 Flat Heart Disease
## 101 Y 1.0 Flat Heart Disease
## 867 N 0.0 Up Normal
## 176 Y 2.0 Flat Heart Disease
## 120 N 0.0 Flat Heart Disease
## 707 N 0.0 Up Heart Disease
## 520 Y 1.0 Up Heart Disease
## 419 Y 1.5 Down Heart Disease
## 787 N 2.0 Flat Heart Disease
## 556 Y 0.0 Flat Heart Disease
## 445 Y 1.5 Flat Heart Disease
## 122 N 0.0 Up Normal
## 639 N 0.1 Up Normal
## 281 N 0.0 Up Normal
## 19 N 1.0 Flat Heart Disease
## 803 N 0.1 Up Normal
## 578 Y 1.9 Flat Heart Disease
## 236 Y 1.0 Flat Normal
## 641 N 0.2 Up Normal
## 254 N 1.0 Up Normal
## 914 N 1.2 Flat Heart Disease
## 848 N 0.0 Up Normal
## 813 N 1.6 Flat Normal
## 722 Y 1.2 Flat Normal
## 5 N 0.0 Up Normal
## 718 N 0.0 Up Normal
## 531 Y 1.1 Flat Heart Disease
## 517 N 0.0 Flat Heart Disease
## 672 Y 3.6 Flat Heart Disease
## 73 N 0.0 Flat Heart Disease
## 710 N 1.0 Up Heart Disease
## 791 N 1.9 Flat Heart Disease
## 181 Y 2.0 Flat Heart Disease
## 871 N 0.4 Up Normal
## 139 Y 0.0 Flat Heart Disease
## 800 N 0.0 Up Normal
## 524 Y 1.0 Flat Heart Disease
## 229 N 0.0 Up Normal
## 860 N 1.5 Up Normal
## 879 N 0.6 Up Normal
## 822 N 0.0 Up Normal
## 117 N 0.0 Flat Heart Disease
## 569 Y 1.5 Down Heart Disease
## 799 N 0.6 Flat Normal
## 859 N 3.6 Down Heart Disease
## 163 N 0.0 Up Normal
## 7 N 0.0 Up Normal
## 103 N 2.0 Flat Heart Disease
## 234 N 0.0 Up Normal
## 884 N 0.4 Flat Normal
## 134 Y 1.5 Flat Heart Disease
## 82 N 0.0 Up Normal
## 56 N 0.0 Up Normal
## 577 Y 3.0 Flat Heart Disease
## 546 N 0.2 Up Normal
## 657 N 0.0 Up Normal
## 167 Y 5.0 Flat Heart Disease
## 585 Y 1.5 Flat Heart Disease
## 658 Y 0.4 Up Normal
## 189 Y 1.0 Flat Normal
## 576 Y 1.2 Flat Heart Disease
## 883 N 1.3 Flat Normal
## 169 N 0.0 Up Normal
## 691 Y 0.0 Flat Heart Disease
## 836 Y 2.8 Flat Heart Disease
## 273 Y 3.0 Flat Heart Disease
## 288 N 0.0 Up Normal
## 684 N 0.3 Flat Normal
## 60 Y 1.0 Flat Heart Disease
## 102 N 0.0 Up Normal
## 193 N 0.0 Up Normal
## 20 N 3.0 Flat Heart Disease
## 819 Y 1.6 Up Heart Disease
## 606 N 1.0 Up Normal
## 589 Y 2.0 Flat Heart Disease
## 231 N 0.0 Up Normal
## 205 N 0.0 Up Normal
## 698 Y 0.8 Up Heart Disease
## 821 N 1.2 Flat Normal
## 486 Y 1.2 Flat Heart Disease
## 184 Y 1.0 Flat Normal
## 258 N 0.0 Up Normal
## 215 Y 1.5 Flat Heart Disease
## 165 N 0.0 Up Normal
## 601 Y 1.0 Flat Normal
## 38 N 0.0 Up Normal
## 13 N 0.0 Up Normal
## 619 Y 0.2 Flat Normal
## 121 N 0.0 Flat Heart Disease
## 513 N -0.1 Up Normal
## 453 Y 1.5 Flat Heart Disease
## 651 Y 0.0 Up Heart Disease
## 203 N 0.0 Up Normal
## 792 Y 4.2 Flat Heart Disease
## 225 N 0.0 Up Normal
## 15 N 0.0 Up Normal
## 88 Y 2.0 Flat Normal
## 235 N 0.0 Up Normal
## 761 Y 2.0 Flat Heart Disease
## 723 N 2.6 Flat Heart Disease
## 8 N 0.0 Up Normal
## 795 N 0.0 Up Normal
## 858 N 0.4 Flat Normal
## 501 Y 4.0 Down Heart Disease
## 69 Y 4.0 Flat Heart Disease
## 823 Y 0.6 Flat Normal
## 538 Y 4.0 Down Heart Disease
## 123 N 0.0 Up Normal
## 503 Y 2.0 Flat Heart Disease
## 157 Y 1.0 Flat Heart Disease
## 127 N 0.0 Up Normal
## 55 Y 1.5 Flat Normal
## 40 Y 1.0 Flat Normal
## 852 N 0.0 Up Normal
## 271 N 0.0 Up Normal
## 703 N 4.2 Down Normal
## 784 Y 0.2 Flat Normal
## 108 N 0.0 Up Normal
## 223 N 0.0 Up Normal
## 851 N 6.2 Down Heart Disease
## 874 N 0.0 Up Heart Disease
## 894 N 0.0 Up Heart Disease
## 4 Y 1.5 Flat Heart Disease
## 591 N 0.2 Up Normal
## 880 N 1.0 Down Heart Disease
## 833 Y 0.0 Up Normal
## 690 N 0.3 Up Normal
## 740 N 0.5 Down Normal
## 676 N 0.2 Up Normal
## 66 N 0.0 Up Normal
## 778 N 0.0 Up Normal
## 491 Y 1.0 Flat Heart Disease
## 216 N 0.0 Up Normal
## 265 Y 1.0 Flat Heart Disease
## 162 Y 0.0 Flat Heart Disease
## 731 N 0.0 Flat Normal
## 862 N 0.6 Up Heart Disease
## 204 N 0.0 Up Normal
## 768 N 0.0 Up Normal
## 449 Y 1.0 Flat Heart Disease
## 80 N 0.0 Flat Heart Disease
## 844 N 1.0 Up Normal
## 278 Y 1.5 Flat Heart Disease
## 114 N 0.0 Up Normal
## 202 N 0.0 Up Normal
## 730 N 0.0 Up Normal
## 647 N 2.3 Up Normal
## 455 Y 1.2 Flat Heart Disease
## 885 Y 1.5 Flat Heart Disease
## 562 N 0.1 Up Normal
## 701 N 0.8 Up Normal
## 696 N 2.0 Flat Heart Disease
## 245 Y 1.0 Flat Heart Disease
## 741 N 0.0 Up Normal
## 76 N 0.0 Up Normal
## 65 N 0.0 Up Normal
## 141 Y 2.5 Flat Heart Disease
## 417 Y 3.0 Flat Heart Disease
## 136 N 0.0 Flat Heart Disease
## 527 Y 1.7 Flat Heart Disease
## 144 N 0.0 Up Normal
## 230 N 0.0 Up Normal
## 889 Y 1.0 Flat Heart Disease
## 85 Y 1.0 Flat Heart Disease
## 724 Y 0.0 Up Heart Disease
## 483 N 2.5 Up Heart Disease
## 277 N 2.0 Flat Heart Disease
## 276 N 0.0 Up Normal
## 714 N 0.2 Up Normal
## 916 Y 1.2 Flat Heart Disease
## 809 Y 0.0 Up Heart Disease
## 678 N 0.0 Up Normal
## 255 Y 2.0 Flat Heart Disease
## 514 Y 1.3 Flat Heart Disease
## 246 N 2.0 Up Normal
## 631 N 1.6 Flat Normal
## 841 N 0.0 Flat Normal
## 807 Y 2.6 Down Heart Disease
## 895 N 0.6 Flat Normal
## 682 Y 0.0 Up Normal
## 646 N 0.4 Flat Heart Disease
## 543 Y 1.5 Flat Heart Disease
## 470 N 2.0 Flat Normal
## 611 N 0.0 Flat Heart Disease
## 599 Y 1.7 Down Heart Disease
## 290 N 0.0 Up Normal
## 279 Y 0.8 Flat Normal
## 219 N 0.0 Up Normal
## 250 Y 1.0 Flat Heart Disease
## 98 N 0.0 Up Normal
## 37 Y 1.5 Flat Heart Disease
## 763 Y 2.0 Flat Heart Disease
## 700 Y 1.5 Flat Normal
## 873 Y 1.2 Flat Heart Disease
## 191 N 0.0 Up Normal
## 824 Y 1.8 Flat Heart Disease
## 754 N 0.7 Up Normal
## 626 N 0.5 Flat Normal
## 637 N 0.2 Flat Normal
## 23 N 0.0 Up Normal
## 112 Y 3.0 Flat Heart Disease
## 882 N 0.0 Up Normal
## 780 N 0.0 Up Normal
## 850 N 1.8 Flat Normal
## 664 Y 0.0 Flat Heart Disease
## 864 N 3.0 Flat Heart Disease
## 869 N 0.6 Up Normal
## 539 Y 2.0 Flat Heart Disease
## 687 Y 0.6 Up Normal
## 262 N 1.0 Up Normal
## 179 N 0.0 Up Normal
## 109 N 0.0 Up Normal
## 238 N 0.0 Flat Heart Disease
## 767 N 1.6 Flat Normal
## 856 Y 1.6 Flat Heart DiseaseValidation Data Frame
Since the total rows of train data minus training data is 368, we have to remove the last line so that the total rows become 367.
validationDF <- validationDF[-c(368),]
validationDF## Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR
## 1 40 M ATA 140 289 0 Normal 172
## 3 37 M ATA 130 283 0 ST 98
## 9 37 M ASY 140 207 0 Normal 130
## 10 48 F ATA 120 284 0 Normal 120
## 11 37 F NAP 130 211 0 Normal 142
## 12 58 M ATA 136 164 0 ST 99
## 14 49 M ASY 140 234 0 Normal 140
## 16 54 F ATA 120 273 0 Normal 150
## 18 43 F ATA 120 201 0 Normal 165
## 21 43 F TA 100 223 0 Normal 142
## 26 36 M NAP 130 209 0 Normal 178
## 28 52 M ATA 120 284 0 Normal 118
## 30 51 M ATA 125 188 0 Normal 145
## 32 56 M NAP 130 167 0 Normal 114
## 33 54 M ASY 125 224 0 Normal 122
## 34 41 M ASY 130 172 0 ST 130
## 36 32 M ATA 125 254 0 Normal 155
## 39 48 F ATA 120 177 1 ST 148
## 41 54 F ATA 150 230 0 Normal 130
## 44 52 M NAP 140 259 0 ST 170
## 48 50 M ATA 140 216 0 Normal 170
## 49 36 M NAP 112 340 0 Normal 184
## 50 41 M ASY 110 289 0 Normal 170
## 51 50 M ASY 130 233 0 Normal 121
## 52 47 F ASY 120 205 0 Normal 98
## 53 45 M ATA 140 224 1 Normal 122
## 57 31 M ASY 120 270 0 Normal 153
## 59 54 M ASY 150 365 0 ST 134
## 61 49 M ATA 100 253 0 Normal 174
## 63 45 M ASY 140 224 0 Normal 144
## 64 46 M ASY 120 277 0 Normal 125
## 71 57 M ATA 140 265 0 ST 145
## 72 44 M ATA 130 215 0 Normal 135
## 74 44 F ASY 120 218 0 ST 115
## 75 55 M ASY 140 268 0 Normal 128
## 78 35 F ASY 140 167 0 Normal 150
## 79 52 M ATA 140 100 0 Normal 138
## 81 55 M NAP 110 277 0 Normal 160
## 83 63 M ASY 150 223 0 Normal 115
## 84 52 M ATA 160 196 0 Normal 165
## 86 66 M ASY 140 139 0 Normal 94
## 89 43 M TA 120 291 0 ST 155
## 90 55 M ASY 140 229 0 Normal 110
## 91 49 F ATA 110 208 0 Normal 160
## 93 52 F ATA 120 210 0 Normal 148
## 94 48 M ASY 160 329 0 Normal 92
## 95 39 F NAP 110 182 0 ST 180
## 97 43 M ATA 142 207 0 Normal 138
## 99 56 M ASY 120 85 0 Normal 140
## 100 41 M ATA 125 269 0 Normal 144
## 105 46 M ASY 118 186 0 Normal 124
## 106 57 M ATA 140 260 1 Normal 140
## 107 48 F ASY 120 254 0 ST 110
## 113 47 M ASY 140 276 1 Normal 125
## 115 49 F NAP 130 207 0 ST 135
## 119 35 F TA 120 160 0 ST 185
## 124 58 F ATA 180 393 0 Normal 110
## 125 58 M ATA 130 230 0 Normal 150
## 126 54 M ATA 120 246 0 Normal 110
## 128 48 F ASY 108 163 0 Normal 175
## 130 42 M NAP 120 228 0 Normal 152
## 132 46 M ASY 110 202 0 Normal 150
## 135 61 F ASY 130 294 0 ST 120
## 140 43 M ASY 150 247 0 Normal 130
## 142 50 M ASY 140 341 0 ST 125
## 143 47 M ASY 160 291 0 ST 158
## 145 56 F ATA 120 279 0 Normal 150
## 146 39 M ASY 110 273 0 Normal 132
## 148 43 F ATA 120 249 0 ST 176
## 149 50 M ATA 120 168 0 Normal 160
## 151 39 M ATA 130 215 0 Normal 120
## 152 48 M ATA 100 159 0 Normal 100
## 153 40 M ATA 130 275 0 Normal 150
## 154 55 M ASY 120 270 0 Normal 140
## 156 56 M ASY 155 342 1 Normal 150
## 158 49 M ASY 140 185 0 Normal 130
## 164 42 M ATA 120 196 0 Normal 150
## 166 46 M TA 140 272 1 Normal 175
## 168 48 M ATA 140 238 0 Normal 118
## 170 58 M NAP 140 179 0 Normal 160
## 171 29 M ATA 120 243 0 Normal 160
## 173 53 M ATA 140 320 0 Normal 162
## 174 49 M NAP 140 187 0 Normal 172
## 175 52 M ASY 140 266 0 Normal 134
## 177 54 M ASY 140 216 0 Normal 105
## 178 59 M ATA 140 287 0 Normal 150
## 180 46 F ASY 130 238 0 Normal 90
## 182 51 M ATA 130 224 0 Normal 150
## 183 52 M ASY 140 404 0 Normal 124
## 185 54 F ATA 160 312 0 Normal 130
## 186 58 M NAP 160 211 1 ST 92
## 187 58 M ATA 130 251 0 Normal 110
## 188 41 M ASY 120 237 1 Normal 138
## 190 53 M ASY 180 285 0 ST 120
## 192 50 M ATA 170 209 0 ST 116
## 194 45 M NAP 135 192 0 Normal 110
## 195 41 F ATA 125 184 0 Normal 180
## 196 62 F TA 160 193 0 Normal 116
## 198 42 M ATA 150 268 0 Normal 136
## 199 53 M ASY 120 246 0 Normal 116
## 201 47 M TA 110 249 0 Normal 150
## 207 35 M ATA 120 308 0 LVH 180
## 208 35 M ATA 110 257 0 Normal 140
## 209 28 M ATA 130 132 0 LVH 185
## 210 54 M ASY 125 216 0 Normal 140
## 211 48 M ASY 106 263 1 Normal 110
## 213 56 M NAP 130 276 0 Normal 128
## 214 56 F NAP 130 219 0 ST 164
## 217 39 M ASY 110 280 0 Normal 150
## 220 29 M ATA 140 263 0 Normal 170
## 222 51 F ASY 160 303 0 Normal 150
## 224 33 M NAP 120 298 0 Normal 185
## 227 53 M NAP 120 195 0 Normal 140
## 228 38 M ASY 92 117 0 Normal 134
## 232 40 M NAP 130 281 0 Normal 167
## 239 48 M ASY 160 355 0 Normal 99
## 241 55 M ATA 145 326 0 Normal 155
## 244 43 F ATA 120 266 0 Normal 118
## 248 48 M ASY 122 275 1 ST 150
## 252 48 M ASY 120 260 0 Normal 115
## 253 61 M ASY 125 292 0 ST 115
## 256 53 F NAP 120 274 0 Normal 130
## 257 55 F ATA 130 394 0 LVH 150
## 259 51 F NAP 150 200 0 Normal 120
## 260 55 F ATA 122 320 0 Normal 155
## 261 46 M ATA 140 275 0 Normal 165
## 263 46 M ASY 120 231 0 Normal 115
## 264 59 M ASY 130 126 0 Normal 125
## 266 54 M ATA 160 305 0 Normal 175
## 267 52 M ASY 130 298 0 Normal 110
## 268 34 M ATA 98 220 0 Normal 150
## 272 32 F ATA 105 198 0 Normal 165
## 274 55 M NAP 120 220 0 LVH 134
## 275 45 F ATA 180 295 0 Normal 180
## 280 54 F ATA 130 253 0 ST 155
## 282 49 M ASY 150 222 0 Normal 122
## 283 51 F NAP 130 220 0 Normal 160
## 285 42 M ASY 140 358 0 Normal 170
## 291 48 M NAP 110 211 0 Normal 138
## 292 47 F ATA 140 257 0 Normal 135
## 293 53 M ASY 130 182 0 Normal 148
## 418 44 M ASY 130 209 0 ST 127
## 420 55 M ASY 142 228 0 ST 149
## 421 66 M NAP 110 213 1 LVH 99
## 423 65 M ASY 150 236 1 ST 105
## 426 60 M ATA 160 267 1 ST 157
## 427 56 M ATA 126 166 0 ST 140
## 432 62 M ASY 120 220 0 ST 86
## 433 63 M ASY 170 177 0 Normal 84
## 446 55 M NAP 136 228 0 ST 124
## 461 57 M ASY 139 277 1 ST 118
## 463 59 M ASY 122 233 0 Normal 117
## 474 60 M NAP 141 316 1 ST 122
## 477 51 M ASY 132 218 1 LVH 139
## 479 57 M ASY 130 311 1 ST 148
## 487 55 M ATA 110 214 1 ST 180
## 488 57 M ASY 140 214 0 ST 144
## 489 65 M TA 140 252 0 Normal 135
## 490 54 M ASY 136 220 0 Normal 140
## 494 51 M NAP 137 339 0 Normal 127
## 495 60 M ASY 142 216 0 Normal 110
## 498 61 M ASY 146 241 0 Normal 148
## 500 62 M ASY 135 297 0 Normal 130
## 504 51 M ASY 132 227 1 ST 138
## 505 62 M ASY 158 210 1 Normal 112
## 506 55 M NAP 136 245 1 ST 131
## 507 75 M ASY 136 225 0 Normal 112
## 508 40 M NAP 106 240 0 Normal 80
## 510 58 M ASY 110 198 0 Normal 110
## 512 63 M ASY 160 267 1 ST 88
## 518 65 M ASY 150 235 0 Normal 120
## 521 64 M ASY 130 223 0 ST 128
## 523 50 M ASY 144 349 0 LVH 120
## 526 45 M NAP 130 236 0 Normal 144
## 529 49 M NAP 131 142 0 Normal 127
## 530 72 M ASY 143 211 0 Normal 109
## 533 55 M ASY 116 186 1 ST 102
## 534 63 M ASY 110 252 0 ST 140
## 535 59 M ASY 125 222 0 Normal 135
## 540 57 M ASY 110 197 0 LVH 100
## 541 62 M NAP 138 204 0 ST 122
## 544 70 M ASY 170 192 0 ST 129
## 548 61 M TA 142 200 1 ST 100
## 550 68 M TA 139 181 1 ST 135
## 551 55 M ASY 172 260 0 Normal 73
## 552 62 M NAP 120 220 0 LVH 86
## 553 71 M NAP 144 221 0 Normal 108
## 554 74 M TA 145 216 1 Normal 116
## 555 53 M NAP 155 175 1 ST 160
## 557 75 M ASY 160 310 1 Normal 112
## 558 56 M NAP 137 208 1 ST 122
## 565 57 M ASY 144 270 1 ST 160
## 566 61 M ASY 141 292 0 ST 115
## 567 41 M ASY 150 171 0 Normal 128
## 568 71 M ASY 130 221 0 ST 115
## 570 55 M ASY 158 217 0 Normal 110
## 571 56 M ASY 128 223 0 ST 119
## 572 69 M ASY 140 110 1 Normal 109
## 575 69 M ASY 142 210 1 ST 112
## 579 57 M ASY 156 173 0 LVH 119
## 581 51 M ASY 131 152 1 LVH 130
## 584 69 M NAP 142 271 0 LVH 126
## 586 57 M ATA 180 285 1 ST 120
## 587 53 M ASY 124 243 0 Normal 122
## 588 37 M NAP 118 240 0 LVH 165
## 590 74 M NAP 140 237 1 Normal 94
## 592 58 M ASY 100 213 0 ST 110
## 594 64 M ASY 130 258 1 LVH 130
## 597 57 M ASY 122 264 0 LVH 100
## 598 55 M NAP 133 185 0 ST 136
## 600 56 M ASY 130 203 1 Normal 98
## 602 61 M NAP 140 284 0 Normal 123
## 603 61 M NAP 120 337 0 Normal 98
## 604 74 M ASY 155 310 0 Normal 112
## 605 68 M NAP 134 254 1 Normal 151
## 607 62 M ASY 160 254 1 ST 108
## 609 62 M ASY 158 170 0 ST 138
## 610 46 M ASY 134 310 0 Normal 126
## 612 62 M TA 135 139 0 ST 137
## 613 55 M ASY 122 223 1 ST 100
## 614 58 M ASY 140 385 1 LVH 135
## 615 62 M ATA 120 254 0 LVH 93
## 616 70 M ASY 130 322 0 LVH 109
## 621 65 M ASY 120 177 0 Normal 140
## 622 56 M NAP 130 256 1 LVH 142
## 623 59 M ASY 110 239 0 LVH 142
## 627 53 M ASY 142 226 0 LVH 111
## 628 44 M NAP 140 235 0 LVH 180
## 629 61 M TA 134 234 0 Normal 145
## 632 46 M ASY 140 311 0 Normal 120
## 634 64 M TA 110 211 0 LVH 144
## 636 67 M ASY 120 229 0 LVH 129
## 638 43 M ASY 115 303 0 Normal 181
## 640 54 F ATA 132 288 1 LVH 159
## 643 51 F NAP 120 295 0 LVH 157
## 644 58 M NAP 112 230 0 LVH 165
## 645 71 F NAP 110 265 1 LVH 130
## 648 37 F NAP 120 215 0 Normal 170
## 649 59 M ASY 170 326 0 LVH 140
## 650 50 M ASY 144 200 0 LVH 126
## 653 59 M TA 160 273 0 LVH 125
## 654 42 M NAP 130 180 0 Normal 150
## 656 40 M ASY 152 223 0 Normal 181
## 659 46 M ATA 101 197 1 Normal 156
## 661 58 M NAP 140 211 1 LVH 165
## 663 44 M ASY 110 197 0 LVH 177
## 665 65 F ASY 150 225 0 LVH 114
## 666 42 M ASY 136 315 0 Normal 125
## 667 52 M ATA 128 205 1 Normal 184
## 670 45 F ATA 130 234 0 LVH 175
## 673 60 F NAP 120 178 1 Normal 96
## 674 59 F ASY 174 249 0 Normal 143
## 677 51 F ASY 130 305 0 Normal 142
## 679 60 F TA 150 240 0 Normal 171
## 681 57 M ASY 150 276 0 LVH 112
## 685 47 M NAP 108 243 0 Normal 152
## 686 61 M ASY 120 260 0 Normal 140
## 688 70 M ATA 156 245 0 LVH 143
## 689 76 F NAP 140 197 0 ST 116
## 692 45 M ASY 104 208 0 LVH 148
## 695 56 M ATA 120 236 0 Normal 178
## 699 41 M NAP 130 214 0 LVH 168
## 704 41 F ATA 126 306 0 Normal 163
## 705 50 M ASY 150 243 0 LVH 128
## 706 59 M ATA 140 221 0 Normal 164
## 708 54 M ASY 124 266 0 LVH 109
## 709 54 M ASY 110 206 0 LVH 108
## 711 47 M ASY 110 275 0 LVH 118
## 712 66 M ASY 120 302 0 LVH 151
## 713 58 M ASY 100 234 0 Normal 156
## 715 50 F ATA 120 244 0 Normal 162
## 717 67 M ASY 120 237 0 Normal 71
## 720 63 M ASY 130 254 0 LVH 147
## 726 55 F ASY 180 327 0 ST 117
## 728 60 F ASY 158 305 0 LVH 161
## 729 54 F NAP 135 304 1 Normal 170
## 732 46 M ASY 120 249 0 LVH 144
## 733 56 F ASY 200 288 1 LVH 133
## 735 56 M ASY 130 283 1 LVH 103
## 737 54 M ASY 122 286 0 LVH 116
## 738 57 M ASY 152 274 0 Normal 88
## 739 65 F NAP 160 360 0 LVH 151
## 742 62 M ASY 120 267 0 Normal 99
## 744 52 M ATA 134 201 0 Normal 158
## 745 60 M ASY 117 230 1 Normal 160
## 747 66 M ASY 112 212 0 LVH 132
## 748 42 M ASY 140 226 0 Normal 178
## 750 54 M NAP 150 232 0 LVH 165
## 751 46 F NAP 142 177 0 LVH 160
## 752 67 F NAP 152 277 0 Normal 172
## 753 56 M ASY 125 249 1 LVH 144
## 756 64 M ASY 145 212 0 LVH 132
## 758 50 M NAP 140 233 0 Normal 163
## 759 51 M TA 125 213 0 LVH 125
## 762 52 M ASY 112 230 0 Normal 160
## 765 41 F NAP 112 268 0 LVH 172
## 766 41 M NAP 112 250 0 Normal 179
## 770 51 F NAP 130 256 0 LVH 149
## 772 55 M ASY 140 217 0 Normal 111
## 773 45 M ATA 128 308 0 LVH 170
## 774 56 M TA 120 193 0 LVH 162
## 775 66 F ASY 178 228 1 Normal 165
## 776 38 M TA 120 231 0 Normal 182
## 779 58 M ASY 128 259 0 LVH 130
## 781 64 F ASY 180 325 0 Normal 154
## 783 53 M NAP 130 197 1 LVH 152
## 785 65 M TA 138 282 1 LVH 174
## 788 67 M ASY 100 299 0 LVH 125
## 789 68 F NAP 120 211 0 LVH 115
## 790 34 M TA 118 182 0 LVH 174
## 793 46 M NAP 150 231 0 Normal 147
## 794 67 M ASY 125 254 1 Normal 163
## 796 42 M NAP 120 240 1 Normal 194
## 801 43 M NAP 130 315 0 Normal 162
## 802 56 M ASY 132 184 0 LVH 105
## 804 62 F ASY 140 394 0 LVH 157
## 805 70 M NAP 160 269 0 Normal 112
## 806 54 M ASY 140 239 0 Normal 160
## 810 48 M NAP 124 255 1 Normal 175
## 811 55 F ATA 135 250 0 LVH 161
## 812 58 F ASY 100 248 0 LVH 122
## 814 69 F TA 140 239 0 Normal 151
## 817 58 M ASY 125 300 0 LVH 171
## 820 55 M ASY 160 289 0 LVH 145
## 825 37 M NAP 130 250 0 Normal 187
## 826 59 M TA 170 288 0 LVH 159
## 827 51 M NAP 125 245 1 LVH 166
## 828 43 F NAP 122 213 0 Normal 165
## 831 41 F ATA 130 204 0 LVH 172
## 832 63 F NAP 135 252 0 LVH 172
## 834 54 M NAP 120 258 0 LVH 147
## 837 65 M ASY 135 254 0 LVH 127
## 838 57 M NAP 150 168 0 Normal 174
## 839 63 M ASY 130 330 1 LVH 132
## 842 62 F NAP 130 263 0 Normal 97
## 846 61 F ASY 145 307 0 LVH 146
## 854 47 M NAP 138 257 0 LVH 156
## 855 52 M ATA 120 325 0 Normal 172
## 857 39 M NAP 140 321 0 LVH 182
## 861 60 M ASY 130 253 0 Normal 144
## 868 44 M ASY 112 290 0 LVH 153
## 870 59 M NAP 150 212 1 Normal 157
## 876 58 F NAP 120 340 0 Normal 172
## 877 60 M ASY 130 206 0 LVH 132
## 878 58 M ATA 120 284 0 LVH 160
## 881 52 M NAP 172 199 1 Normal 162
## 886 53 F NAP 128 216 0 LVH 115
## 887 52 M NAP 138 223 0 Normal 169
## 888 43 M ASY 132 247 1 LVH 143
## 890 59 M TA 134 204 0 Normal 162
## 891 64 M TA 170 227 0 LVH 155
## 893 39 F NAP 138 220 0 Normal 152
## 896 57 M ASY 110 335 0 Normal 143
## 898 55 F ASY 128 205 0 ST 130
## 899 35 M ATA 122 192 0 Normal 174
## 900 61 M ASY 148 203 0 Normal 161
## 901 58 M ASY 114 318 0 ST 140
## 903 58 M ATA 125 220 0 Normal 144
## 905 56 M ATA 120 240 0 Normal 169
## 906 67 M NAP 152 212 0 LVH 150
## 907 55 F ATA 132 342 0 Normal 166
## 908 44 M ASY 120 169 0 Normal 144
## 909 63 M ASY 140 187 0 LVH 144
## 910 63 F ASY 124 197 0 Normal 136
## 912 59 M ASY 164 176 1 LVH 90
## 913 57 F ASY 140 241 0 Normal 123
## 915 68 M ASY 144 193 1 Normal 141
## ExerciseAngina Oldpeak ST_Slope HeartDisease
## 1 N 0.0 Up Normal
## 3 N 0.0 Up Normal
## 9 Y 1.5 Flat Heart Disease
## 10 N 0.0 Up Normal
## 11 N 0.0 Up Normal
## 12 Y 2.0 Flat Heart Disease
## 14 Y 1.0 Flat Heart Disease
## 16 N 1.5 Flat Normal
## 18 N 0.0 Up Normal
## 21 N 0.0 Up Normal
## 26 N 0.0 Up Normal
## 28 N 0.0 Up Normal
## 30 N 0.0 Up Normal
## 32 N 0.0 Up Normal
## 33 N 2.0 Flat Heart Disease
## 34 N 2.0 Flat Heart Disease
## 36 N 0.0 Up Normal
## 39 N 0.0 Up Normal
## 41 N 0.0 Up Normal
## 44 N 0.0 Up Normal
## 48 N 0.0 Up Normal
## 49 N 1.0 Flat Normal
## 50 N 0.0 Flat Heart Disease
## 51 Y 2.0 Flat Heart Disease
## 52 Y 2.0 Flat Heart Disease
## 53 N 0.0 Up Normal
## 57 Y 1.5 Flat Heart Disease
## 59 N 1.0 Up Normal
## 61 N 0.0 Up Normal
## 63 N 0.0 Up Normal
## 64 Y 1.0 Flat Heart Disease
## 71 Y 1.0 Flat Heart Disease
## 72 N 0.0 Up Normal
## 74 N 0.0 Up Normal
## 75 Y 1.5 Flat Heart Disease
## 78 N 0.0 Up Normal
## 79 Y 0.0 Up Normal
## 81 N 0.0 Up Normal
## 83 N 0.0 Flat Heart Disease
## 84 N 0.0 Up Normal
## 86 Y 1.0 Flat Heart Disease
## 89 N 0.0 Flat Heart Disease
## 90 Y 0.5 Flat Normal
## 91 N 0.0 Up Normal
## 93 N 0.0 Up Normal
## 94 Y 1.5 Flat Heart Disease
## 95 N 0.0 Up Normal
## 97 N 0.0 Up Normal
## 99 N 0.0 Up Normal
## 100 N 0.0 Up Normal
## 105 N 0.0 Flat Heart Disease
## 106 N 0.0 Up Normal
## 107 N 0.0 Up Normal
## 113 Y 0.0 Up Normal
## 115 N 0.0 Up Normal
## 119 N 0.0 Up Normal
## 124 Y 1.0 Flat Heart Disease
## 125 N 0.0 Up Normal
## 126 N 0.0 Up Normal
## 128 N 2.0 Up Normal
## 130 Y 1.5 Flat Normal
## 132 Y 0.0 Flat Heart Disease
## 135 Y 1.0 Flat Normal
## 140 Y 2.0 Flat Heart Disease
## 142 Y 2.5 Flat Heart Disease
## 143 Y 3.0 Flat Heart Disease
## 145 N 1.0 Flat Heart Disease
## 146 N 0.0 Up Normal
## 148 N 0.0 Up Normal
## 149 N 0.0 Up Normal
## 151 N 0.0 Up Normal
## 152 N 0.0 Up Normal
## 153 N 0.0 Up Normal
## 154 N 0.0 Up Normal
## 156 Y 3.0 Flat Heart Disease
## 158 N 0.0 Up Normal
## 164 N 0.0 Up Normal
## 166 N 2.0 Flat Heart Disease
## 168 N 0.0 Up Normal
## 170 N 0.0 Up Normal
## 171 N 0.0 Up Normal
## 173 N 0.0 Up Normal
## 174 N 0.0 Up Normal
## 175 Y 2.0 Flat Heart Disease
## 177 N 1.5 Flat Heart Disease
## 178 N 0.0 Up Normal
## 180 N 0.0 Up Normal
## 182 N 0.0 Up Normal
## 183 Y 2.0 Flat Heart Disease
## 185 N 0.0 Up Normal
## 186 N 0.0 Flat Heart Disease
## 187 N 0.0 Up Normal
## 188 Y 1.0 Flat Heart Disease
## 190 Y 1.5 Flat Heart Disease
## 192 N 0.0 Up Normal
## 194 N 0.0 Up Normal
## 195 N 0.0 Up Normal
## 196 N 0.0 Up Normal
## 198 N 0.0 Up Normal
## 199 Y 0.0 Flat Heart Disease
## 201 N 0.0 Up Normal
## 207 N 0.0 Up Normal
## 208 N 0.0 Flat Heart Disease
## 209 N 0.0 Up Normal
## 210 N 0.0 Flat Heart Disease
## 211 N 0.0 Flat Heart Disease
## 213 Y 1.0 Up Normal
## 214 N 0.0 Up Normal
## 217 N 0.0 Flat Heart Disease
## 220 N 0.0 Up Normal
## 222 Y 1.0 Flat Heart Disease
## 224 N 0.0 Up Normal
## 227 N 0.0 Up Normal
## 228 Y 2.5 Flat Heart Disease
## 232 N 0.0 Up Normal
## 239 Y 2.0 Flat Heart Disease
## 241 N 0.0 Up Normal
## 244 N 0.0 Up Normal
## 248 Y 2.0 Down Heart Disease
## 252 N 2.0 Flat Heart Disease
## 253 Y 0.0 Up Normal
## 256 N 0.0 Up Normal
## 257 N 0.0 Up Normal
## 259 N 0.5 Up Normal
## 260 N 0.0 Up Normal
## 261 Y 0.0 Up Normal
## 263 Y 0.0 Flat Heart Disease
## 264 N 0.0 Flat Heart Disease
## 266 N 0.0 Up Normal
## 267 Y 1.0 Flat Heart Disease
## 268 N 0.0 Up Normal
## 272 N 0.0 Up Normal
## 274 N 0.0 Up Normal
## 275 N 0.0 Up Normal
## 280 N 0.0 Up Normal
## 282 N 2.0 Flat Heart Disease
## 283 Y 2.0 Up Normal
## 285 N 0.0 Up Normal
## 291 N 0.0 Up Normal
## 292 N 1.0 Up Normal
## 293 N 0.0 Up Normal
## 418 N 0.0 Up Normal
## 420 Y 2.5 Up Heart Disease
## 421 Y 1.3 Flat Normal
## 423 Y 0.0 Flat Heart Disease
## 426 N 0.5 Flat Heart Disease
## 427 N 0.0 Up Normal
## 432 N 0.0 Up Normal
## 433 Y 2.5 Down Heart Disease
## 446 Y 1.6 Flat Heart Disease
## 461 Y 1.9 Flat Heart Disease
## 463 Y 1.3 Down Heart Disease
## 474 Y 1.7 Flat Heart Disease
## 477 N 0.1 Up Normal
## 479 Y 2.0 Flat Heart Disease
## 487 N 0.4 Up Normal
## 488 Y 2.0 Flat Heart Disease
## 489 N 0.3 Up Normal
## 490 Y 3.0 Flat Heart Disease
## 494 Y 1.7 Flat Heart Disease
## 495 Y 2.5 Flat Heart Disease
## 498 Y 3.0 Down Heart Disease
## 500 Y 1.0 Flat Heart Disease
## 504 N 0.2 Up Normal
## 505 Y 3.0 Down Heart Disease
## 506 Y 1.2 Flat Heart Disease
## 507 Y 3.0 Flat Heart Disease
## 508 Y 0.0 Up Normal
## 510 N 0.0 Flat Heart Disease
## 512 Y 2.0 Flat Heart Disease
## 518 Y 1.5 Flat Heart Disease
## 521 N 0.5 Flat Normal
## 523 Y 1.0 Up Heart Disease
## 526 N 0.1 Up Normal
## 529 Y 1.5 Flat Heart Disease
## 530 Y 1.4 Flat Heart Disease
## 533 N 0.0 Flat Heart Disease
## 534 Y 2.0 Flat Heart Disease
## 535 Y 2.5 Down Heart Disease
## 540 N 0.0 Up Normal
## 541 Y 1.2 Flat Heart Disease
## 544 Y 3.0 Down Heart Disease
## 548 N 1.5 Down Heart Disease
## 550 N 0.2 Up Normal
## 551 N 2.0 Flat Heart Disease
## 552 N 0.0 Up Normal
## 553 Y 1.8 Flat Heart Disease
## 554 Y 1.8 Flat Heart Disease
## 555 N 0.3 Up Normal
## 557 Y 2.0 Down Normal
## 558 Y 1.8 Flat Heart Disease
## 565 Y 2.0 Flat Heart Disease
## 566 Y 1.7 Flat Heart Disease
## 567 Y 1.5 Flat Normal
## 568 Y 0.0 Flat Heart Disease
## 570 Y 2.5 Flat Heart Disease
## 571 Y 2.0 Down Heart Disease
## 572 Y 1.5 Flat Heart Disease
## 575 Y 1.5 Flat Heart Disease
## 579 Y 3.0 Down Heart Disease
## 581 Y 1.0 Flat Heart Disease
## 584 N 0.3 Up Normal
## 586 N 0.8 Flat Heart Disease
## 587 Y 2.0 Flat Heart Disease
## 588 N 1.0 Flat Normal
## 590 N 0.0 Flat Heart Disease
## 592 N 0.0 Up Normal
## 594 N 0.0 Flat Heart Disease
## 597 N 0.0 Flat Heart Disease
## 598 N 0.2 Up Normal
## 600 N 1.5 Flat Heart Disease
## 602 Y 1.3 Flat Heart Disease
## 603 Y 0.0 Flat Heart Disease
## 604 Y 1.5 Down Heart Disease
## 605 Y 0.0 Up Normal
## 607 Y 3.0 Flat Heart Disease
## 609 Y 0.0 Flat Heart Disease
## 610 N 0.0 Flat Heart Disease
## 612 N 0.2 Up Normal
## 613 N 0.0 Flat Heart Disease
## 614 N 0.3 Up Normal
## 615 Y 0.0 Flat Heart Disease
## 616 N 2.4 Flat Heart Disease
## 621 N 0.4 Up Normal
## 622 Y 0.6 Flat Heart Disease
## 623 Y 1.2 Flat Heart Disease
## 627 Y 0.0 Up Normal
## 628 N 0.0 Up Normal
## 629 N 2.6 Flat Heart Disease
## 632 Y 1.8 Flat Heart Disease
## 634 Y 1.8 Flat Normal
## 636 Y 2.6 Flat Heart Disease
## 638 N 1.2 Flat Normal
## 640 Y 0.0 Up Normal
## 643 N 0.6 Up Normal
## 644 N 2.5 Flat Heart Disease
## 645 N 0.0 Up Normal
## 648 N 0.0 Up Normal
## 649 Y 3.4 Down Heart Disease
## 650 Y 0.9 Flat Heart Disease
## 653 N 0.0 Up Heart Disease
## 654 N 0.0 Up Normal
## 656 N 0.0 Up Heart Disease
## 659 N 0.0 Up Normal
## 661 N 0.0 Up Normal
## 663 N 0.0 Up Heart Disease
## 665 N 1.0 Flat Heart Disease
## 666 Y 1.8 Flat Heart Disease
## 667 N 0.0 Up Normal
## 670 N 0.6 Flat Normal
## 673 N 0.0 Up Normal
## 674 Y 0.0 Flat Heart Disease
## 677 Y 1.2 Flat Heart Disease
## 679 N 0.9 Up Normal
## 681 Y 0.6 Flat Heart Disease
## 685 N 0.0 Up Heart Disease
## 686 Y 3.6 Flat Heart Disease
## 688 N 0.0 Up Normal
## 689 N 1.1 Flat Normal
## 692 Y 3.0 Flat Normal
## 695 N 0.8 Up Normal
## 699 N 2.0 Flat Normal
## 704 N 0.0 Up Normal
## 705 N 2.6 Flat Heart Disease
## 706 Y 0.0 Up Normal
## 708 Y 2.2 Flat Heart Disease
## 709 Y 0.0 Flat Heart Disease
## 711 Y 1.0 Flat Heart Disease
## 712 N 0.4 Flat Normal
## 713 N 0.1 Up Heart Disease
## 715 N 1.1 Up Normal
## 717 N 1.0 Flat Heart Disease
## 720 N 1.4 Flat Heart Disease
## 726 Y 3.4 Flat Heart Disease
## 728 N 0.0 Up Heart Disease
## 729 N 0.0 Up Normal
## 732 N 0.8 Up Heart Disease
## 733 Y 4.0 Down Heart Disease
## 735 Y 1.6 Down Heart Disease
## 737 Y 3.2 Flat Heart Disease
## 738 Y 1.2 Flat Heart Disease
## 739 N 0.8 Up Normal
## 742 Y 1.8 Flat Heart Disease
## 744 N 0.8 Up Normal
## 745 Y 1.4 Up Heart Disease
## 747 Y 0.1 Up Heart Disease
## 748 N 0.0 Up Normal
## 750 N 1.6 Up Normal
## 751 Y 1.4 Down Normal
## 752 N 0.0 Up Normal
## 753 Y 1.2 Flat Heart Disease
## 756 N 2.0 Flat Heart Disease
## 758 N 0.6 Flat Heart Disease
## 759 Y 1.4 Up Normal
## 762 N 0.0 Up Heart Disease
## 765 Y 0.0 Up Normal
## 766 N 0.0 Up Normal
## 770 N 0.5 Up Normal
## 772 Y 5.6 Down Heart Disease
## 773 N 0.0 Up Normal
## 774 N 1.9 Flat Normal
## 775 Y 1.0 Flat Heart Disease
## 776 Y 3.8 Flat Heart Disease
## 779 Y 3.0 Flat Heart Disease
## 781 Y 0.0 Up Normal
## 783 N 1.2 Down Normal
## 785 N 1.4 Flat Heart Disease
## 788 Y 0.9 Flat Heart Disease
## 789 N 1.5 Flat Normal
## 790 N 0.0 Up Normal
## 793 N 3.6 Flat Heart Disease
## 794 N 0.2 Flat Heart Disease
## 796 N 0.8 Down Normal
## 801 N 1.9 Up Normal
## 802 Y 2.1 Flat Heart Disease
## 804 N 1.2 Flat Normal
## 805 Y 2.9 Flat Heart Disease
## 806 N 1.2 Up Normal
## 810 N 0.0 Up Normal
## 811 N 1.4 Flat Normal
## 812 N 1.0 Flat Normal
## 814 N 1.8 Up Normal
## 817 N 0.0 Up Heart Disease
## 820 Y 0.8 Flat Heart Disease
## 825 N 3.5 Down Normal
## 826 N 0.2 Flat Heart Disease
## 827 N 2.4 Flat Normal
## 828 N 0.2 Flat Normal
## 831 N 1.4 Up Normal
## 832 N 0.0 Up Normal
## 834 N 0.4 Flat Normal
## 837 N 2.8 Flat Heart Disease
## 838 N 1.6 Up Normal
## 839 Y 1.8 Up Heart Disease
## 842 N 1.2 Flat Heart Disease
## 846 Y 1.0 Flat Heart Disease
## 854 N 0.0 Up Normal
## 855 N 0.2 Up Normal
## 857 N 0.0 Up Normal
## 861 Y 1.4 Up Heart Disease
## 868 N 0.0 Up Heart Disease
## 870 N 1.6 Up Normal
## 876 N 0.0 Up Normal
## 877 Y 2.4 Flat Heart Disease
## 878 N 1.8 Flat Heart Disease
## 881 N 0.5 Up Normal
## 886 N 0.0 Up Normal
## 887 N 0.0 Up Normal
## 888 Y 0.1 Flat Heart Disease
## 890 N 0.8 Up Heart Disease
## 891 N 0.6 Flat Normal
## 893 N 0.0 Flat Normal
## 896 Y 3.0 Flat Heart Disease
## 898 Y 2.0 Flat Heart Disease
## 899 N 0.0 Up Normal
## 900 N 0.0 Up Heart Disease
## 901 N 4.4 Down Heart Disease
## 903 N 0.4 Flat Normal
## 905 N 0.0 Down Normal
## 906 N 0.8 Flat Heart Disease
## 907 N 1.2 Up Normal
## 908 Y 2.8 Down Heart Disease
## 909 Y 4.0 Up Heart Disease
## 910 Y 0.0 Flat Heart Disease
## 912 N 1.0 Flat Heart Disease
## 913 Y 0.2 Flat Heart Disease
## 915 N 3.4 Flat Heart DiseaseAfter we explore the heart data set, we can conclude:
Heart disease is the dependent variable and other columns are the variables that cause an effect to dependent variable (independent variables)
We can diagnose a patient has heart disease or not through a series of examinations and analyze it based on the correlation of dependent and independent variables
We will not remove any column since all columns can affect the possibility of people to get heart disease
Chest Pain Type, Exercise Angina, and ST slope are greatly affects a person’s chances of having heart disease
Sex, Cholesterol, and Oldpeak affect the chances of having heart disease
Age, Fasting BS, Resting BP, Max HR, and Resting ECG do not really affect a person’s chances of having heart disease
Modelling
Make the Generalized Linear Models
From the result, we can get:
The output coefficient estimates show the average change in the response variable is log odds that results from an increase of one unit in each predictor variable.
We can get a sense of the coefficient estimates variability from the standard error.
The z value is calculated by dividing the coefficient estimate by the standard error.
AIC (Akaike Information Criteria) is equivalent to R2 in logistic regression. The smaller AIC value indicates that the model is closer to the truth.AIC (Akaike Information Criteria) is equivalent to R2 in logistic regression. The smaller AIC value indicates that the model is closer to the truth.
Null deviance means only fits the model with the intercept.
Residual deviance means model with all the variables. The smaller residual deviance value indicates that the model is closer to the truth.
Number of fisher scoring iterations means the number of iterations before converging.
Significant codes are refers to how the independent variables effects the dependent variable.
The chi-square statistics is null deviance - residual deviance
model1 <- glm(HeartDisease~.,family = binomial(link = "logit"), trainingDF)
summary(model1)##
## Call:
## glm(formula = HeartDisease ~ ., family = binomial(link = "logit"),
## data = trainingDF)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3004 -0.4794 0.1224 0.4561 2.6113
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.707955 2.521216 1.867 0.06185 .
## Age -0.023952 0.019737 -1.214 0.22491
## SexM -1.889817 0.404647 -4.670 3.01e-06 ***
## ChestPainTypeATA 1.354895 0.477136 2.840 0.00452 **
## ChestPainTypeNAP 0.827989 0.415526 1.993 0.04630 *
## ChestPainTypeTA 2.131007 0.742314 2.871 0.00409 **
## RestingBP -0.005194 0.009856 -0.527 0.59818
## Cholesterol -0.004577 0.003390 -1.350 0.17701
## FastingBS -0.861197 0.499344 -1.725 0.08459 .
## RestingECGNormal 0.665522 0.406228 1.638 0.10136
## RestingECGST 0.523609 0.575682 0.910 0.36306
## MaxHR -0.002206 0.008229 -0.268 0.78868
## ExerciseAnginaY -0.950047 0.369222 -2.573 0.01008 *
## Oldpeak -0.537123 0.194354 -2.764 0.00572 **
## ST_SlopeFlat -0.805579 0.823483 -0.978 0.32795
## ST_SlopeUp 1.261526 0.866009 1.457 0.14520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 507.79 on 366 degrees of freedom
## Residual deviance: 259.16 on 351 degrees of freedom
## AIC: 291.16
##
## Number of Fisher Scoring iterations: 5From the significant code, we can see that sex is greatly affect, also Chest Pain Type affects the chances of having heart disease.
model2 <- glm(HeartDisease~ChestPainType + ExerciseAngina + ST_Slope,family = binomial(link = "logit"), trainingDF)
summary(model2)##
## Call:
## glm(formula = HeartDisease ~ ChestPainType + ExerciseAngina +
## ST_Slope, family = binomial(link = "logit"), data = trainingDF)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3946 -0.4588 0.3422 0.6677 2.1464
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.3434 0.6989 -1.922 0.054588 .
## ChestPainTypeATA 1.5109 0.4206 3.592 0.000328 ***
## ChestPainTypeNAP 0.9771 0.3646 2.680 0.007365 **
## ChestPainTypeTA 1.4415 0.6433 2.241 0.025038 *
## ExerciseAnginaY -1.4211 0.3191 -4.454 8.44e-06 ***
## ST_SlopeFlat 0.5664 0.6882 0.823 0.410507
## ST_SlopeUp 2.6411 0.7063 3.740 0.000184 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 507.79 on 366 degrees of freedom
## Residual deviance: 308.21 on 360 degrees of freedom
## AIC: 322.21
##
## Number of Fisher Scoring iterations: 5From the significant codes, Chest Pain Type, Exercise Angina, and ST Slope up are greatly affects the chances of having heart disease
From the both generalized linear model, we can conclude that the first model using all columns are better than the top 3 columns that have high effect to the heart disease possibility as we can see the number of AIC score of the first model is less than the second.
Predict and Evaluate
Make the predictions and evaluations
predictionLogistic1 <- predict(model1, trainingDF,type = "response")
predictionLogistic3 <- predict(model1, validationDF,type = "response")
predictionLogistic2 <- predict(model2, trainingDF ,type = "response")
predictionLogistic4 <- predict(model2, validationDF ,type = "response")Receiver Operating Characteristics Plot
The higher the True Positive Rate and the smaller the False Positive Rate, the better the threshold. Thus, the closer the positive rate is close to 1 and the false positive rate is close to zero means the better prediction and performance.
evaluation1 <- prediction(predictionLogistic1, trainingDF$HeartDisease)
perform1 <- performance(evaluation1, measure = "tpr", x.measure = "fpr")
plot(perform1,
colorize=TRUE,
lwd= 3,
main= "ROC Plot from model 1 using training data frame")
evaluation3 <- prediction(predictionLogistic3, validationDF$HeartDisease)
perform3 <- performance(evaluation3, measure = "tpr", x.measure = "fpr")
plot(perform3,
colorize=TRUE,
lwd= 3,
main= "ROC Plot from model 1 using validation data frame")
evaluation2 <- prediction(predictionLogistic2, trainingDF$HeartDisease)
perform2 <- performance(evaluation2, measure = "tpr", x.measure = "fpr")
plot(perform2,
colorize=TRUE,
lwd= 3,
main= "ROC Plot from model 2 using training data frame")
evaluation4 <- prediction(predictionLogistic4, validationDF$HeartDisease)
perform4 <- performance(evaluation4, measure = "tpr", x.measure = "fpr")
plot(perform4,
colorize=TRUE,
lwd= 3,
main= "ROC Plot from model 2 using validation data frame")
Our ROC plots are good since almost all plots are close to 1 in their positive rate.
Check the Area Under the Curve Score
The capacity of a classifier to differentiate between classes is measured by the Area Under the Curve (AUC), which is used as a summary of the ROC curve.
auc1 <- performance(evaluation1, measure = "auc")
auc1 <- auc1@y.values[[1]]
sprintf("The AUC Score from model 1 using training data is %f", auc1)## [1] "The AUC Score from model 1 using training data is 0.923233"auc3 <- performance(evaluation3, measure = "auc")
auc3 <- auc3@y.values[[1]]
sprintf("The AUC Score from model 1 using validation data is %f", auc3)## [1] "The AUC Score from model 1 using validation data is 0.936578"auc2 <- performance(evaluation2, measure = "auc")
auc2 <- auc2@y.values[[1]]
sprintf("The AUC Score from model 2 using training data is %f", auc2)## [1] "The AUC Score from model 2 using training data is 0.885787"auc4 <- performance(evaluation4, measure = "auc")
auc4 <- auc4@y.values[[1]]
sprintf("The AUC Score from model 2 using validation data is %f", auc4)## [1] "The AUC Score from model 2 using validation data is 0.934139"We can see that both training and validation data have high scores of AUC, so that the model is not overfitting. The higher the AUC score, the better the model performance we make. From the AUC scores above, we can see that our models are good as almost all get more than 0.9 which is very close to 1 (perfect). From the AUC score, we can conclude that the first model is better which is it contains all of the dependent variables. Moreover, the second model is good also since with only three dependent variables can give AUC score more than 0.9 so this three variables have a great effects to the chances of having heart disease.
Decision Tree
A decision tree is a graph that displays options and their outcomes as a tree. The edges of the graph indicate the criteria or rules for making decisions, whereas the nodes in the graph represent an event or a choice.
modelDecisionTree1 <- rpart(HeartDisease~., data = trainingDF, method = "class")
modelDecisionTree1## n= 367
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 367 174 Normal (0.47411444 0.52588556)
## 2) ST_Slope=Down,Flat 199 51 Heart Disease (0.74371859 0.25628141)
## 4) Sex=M 152 25 Heart Disease (0.83552632 0.16447368)
## 8) ChestPainType=ASY,NAP 131 15 Heart Disease (0.88549618 0.11450382) *
## 9) ChestPainType=ATA,TA 21 10 Heart Disease (0.52380952 0.47619048)
## 18) Cholesterol>=245.5 10 2 Heart Disease (0.80000000 0.20000000) *
## 19) Cholesterol< 245.5 11 3 Normal (0.27272727 0.72727273) *
## 5) Sex=F 47 21 Normal (0.44680851 0.55319149)
## 10) Cholesterol>=243.5 25 9 Heart Disease (0.64000000 0.36000000)
## 20) Oldpeak>=1.35 11 1 Heart Disease (0.90909091 0.09090909) *
## 21) Oldpeak< 1.35 14 6 Normal (0.42857143 0.57142857) *
## 11) Cholesterol< 243.5 22 5 Normal (0.22727273 0.77272727) *
## 3) ST_Slope=Up 168 26 Normal (0.15476190 0.84523810)
## 6) ExerciseAngina=Y 22 9 Heart Disease (0.59090909 0.40909091)
## 12) Oldpeak>=0.7 9 1 Heart Disease (0.88888889 0.11111111) *
## 13) Oldpeak< 0.7 13 5 Normal (0.38461538 0.61538462) *
## 7) ExerciseAngina=N 146 13 Normal (0.08904110 0.91095890) *rpart.plot(modelDecisionTree1)
modelDecisionTree2 <- rpart(HeartDisease~ Age + ChestPainType + ExerciseAngina + ST_Slope, data = trainingDF, method = "class")
modelDecisionTree2## n= 367
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 367 174 Normal (0.4741144 0.5258856)
## 2) ST_Slope=Down,Flat 199 51 Heart Disease (0.7437186 0.2562814)
## 4) ChestPainType=ASY 134 20 Heart Disease (0.8507463 0.1492537) *
## 5) ChestPainType=ATA,NAP,TA 65 31 Heart Disease (0.5230769 0.4769231)
## 10) ExerciseAngina=Y 20 6 Heart Disease (0.7000000 0.3000000) *
## 11) ExerciseAngina=N 45 20 Normal (0.4444444 0.5555556)
## 22) ChestPainType=NAP 23 10 Heart Disease (0.5652174 0.4347826)
## 44) Age>=55.5 9 2 Heart Disease (0.7777778 0.2222222) *
## 45) Age< 55.5 14 6 Normal (0.4285714 0.5714286) *
## 23) ChestPainType=ATA,TA 22 7 Normal (0.3181818 0.6818182) *
## 3) ST_Slope=Up 168 26 Normal (0.1547619 0.8452381)
## 6) ExerciseAngina=Y 22 9 Heart Disease (0.5909091 0.4090909)
## 12) Age>=57.5 10 2 Heart Disease (0.8000000 0.2000000) *
## 13) Age< 57.5 12 5 Normal (0.4166667 0.5833333) *
## 7) ExerciseAngina=N 146 13 Normal (0.0890411 0.9109589) *#fancyRpartPlot(modelDecisionTree2)
rpart.plot(modelDecisionTree2)
From both of the decision tree, we can conclude :
People with flat ST Slope have high possibility to get heart disease, yet people with up ST Slope tend to be normal
Chest Pain Type, Max HR, and Exercise Angina enough to affect a person’s chances of getting heart disease
mdt1 <- modelDecisionTree1$variable.importance
mdt2 <- modelDecisionTree2$variable.importance
mdt1## ST_Slope Oldpeak ExerciseAngina ChestPainType MaxHR
## 63.196497 41.989467 36.921640 26.793958 23.599508
## Age Sex Cholesterol RestingBP RestingECG
## 16.841365 10.848938 9.038381 3.823849 1.445230mdt2## ST_Slope ExerciseAngina ChestPainType Age
## 63.32126 40.31660 31.02869 16.10086The data above give us information about the importance variable in our model decision tree. The first model decision tree importance variables are ST Slope, Oldpeak, and Exercise Angina. Moreover, ST Slope is strongly influence the likelihood of heart disease in the second model decision tree
predictionDecisionTree1 <- predict(modelDecisionTree1, validationDF, type = "class")
predictionDecisionTree2 <- predict(modelDecisionTree2, validationDF, type = "class")Confusion Matrix
confusionMatrix1 <- table(predictionDecisionTree1, validationDF$HeartDisease)
confusionMatrix1##
## predictionDecisionTree1 Heart Disease Normal
## Heart Disease 145 22
## Normal 31 169confusionMatrix2 <- table(predictionDecisionTree2, validationDF$HeartDisease)
confusionMatrix2##
## predictionDecisionTree2 Heart Disease Normal
## Heart Disease 149 20
## Normal 27 171The diagonal from top right to bottom left refers to the number of correct predictions
Check the accuracy
sum(diag(confusionMatrix1))## [1] 3141 - sum(diag(confusionMatrix1))## [1] -313sum(diag(confusionMatrix2))## [1] 3201 - sum(diag(confusionMatrix2))## [1] -319sprintf("The total of correct predictions from the first prediction tree is %d", sum(diag(confusionMatrix1)))## [1] "The total of correct predictions from the first prediction tree is 314"sprintf("The total of correct predictions from the second prediction tree is %d", sum(diag(confusionMatrix2)))## [1] "The total of correct predictions from the second prediction tree is 320"Shows the accuracy to predict each variable
accuracy1 <- diag(confusionMatrix1)/rowSums(confusionMatrix1) * 100
accuracy1## Heart Disease Normal
## 86.82635 84.50000accuracy2 <- diag(confusionMatrix2)/rowSums(confusionMatrix2) * 100
accuracy2## Heart Disease Normal
## 88.16568 86.36364Shows the overall accuracy
overallAccuracy1 <- sum(diag(confusionMatrix1)) / sum(confusionMatrix1) * 100
sprintf("The overall accuracy of the first prediction tree is %f", overallAccuracy1)## [1] "The overall accuracy of the first prediction tree is 85.558583"overallAccuracy2 <- sum(diag(confusionMatrix2)) / sum(confusionMatrix2) * 100
sprintf("The overall accuracy of the second prediction tree is %f", overallAccuracy2)## [1] "The overall accuracy of the second prediction tree is 87.193460"The higher the overall accuracy indicates that the decision tree can predict more accurately. Since the overall accuracy is more than 80%, so we can say that our decision tree is quite accurate.