UAS
2501970531_Sekar Alisha Firdaus
2022-07-15
#Link https://drive.google.com/drive/folders/14J1pBwILzMvbLsB7Fh-iQo3J0tn1xs3v?usp=sharing
#Load Libraries and Data
library(ROCR)## Warning: package 'ROCR' was built under R version 4.1.3library(rpart)
library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.1.3heart <- read.csv("C:/Users/unise/Documents/BINUS/Data Mining and Visualization/[LEC]_UAS_DTSC6005001_Data Mining and Visualization/heartData.csv")
head(heart)## 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
## ExerciseAngina Oldpeak ST_Slope HeartDisease
## 1 N 0.0 Up 0
## 2 N 1.0 Flat 1
## 3 N 0.0 Up 0
## 4 Y 1.5 Flat 1
## 5 N 0.0 Up 0
## 6 N 0.0 Up 0Dataset ini memiliki 12 kolom, Age: Umur. Sex: Jenis kelamin. ChestPainType: Jenis nyeri dada. RestingBP: Berapa tekanan darah saat dalam keadaan normal. Cholesterol: Tingkat kolesterol. FastingBS: Berapa gula darah saat puasa. MaxHR: Maksimal tekanan ExerciseAngina: Oldpeak: ST_Slope: HeartDisease: Apakah responden memiliki penyakit jantung atau tidak. #Exploratory Data Analysis
dim(heart)## [1] 918 12Dataset terdiri dari 918 baris dan 12 kolom.
sapply(heart,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 0Setiap kolom dalam dataset tersebut tidak memiliki missing values.
sapply(heart,function(x)length(unique(x)))## Age Sex ChestPainType RestingBP Cholesterol
## 50 2 4 67 222
## FastingBS RestingECG MaxHR ExerciseAngina Oldpeak
## 2 3 119 2 53
## ST_Slope HeartDisease
## 3 2Terdapat 50 macam umur dalam dataset ini. Terdapat 2 macam jenis kelamin dalam dataset ini. Terdapat 4 macam tipe dalam dataset ini. Terdapat 67 macam tekanan darah normal. Terdapat 222 macam kolesterol. Terdapat 2 macam gula darah saat puasa. Terdapat 3 macam ECG. Terdapat 119 macam detak jantung. Terdapat 2 macam Angina, apakah memiliki Angina atau tidak. Terdapat 53 macam Oldpeak. Terdapat 3 macam ST_Slope. Terdapat 2 macam penyakit jantung, apakah memiliki penyakit jantung atau tidak.
#Data Preparation
heart <- subset(heart, select = c(1,2,8,9,10,11,12))
heart## Age Sex MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
## 1 40 M 172 N 0.0 Up 0
## 2 49 F 156 N 1.0 Flat 1
## 3 37 M 98 N 0.0 Up 0
## 4 48 F 108 Y 1.5 Flat 1
## 5 54 M 122 N 0.0 Up 0
## 6 39 M 170 N 0.0 Up 0
## 7 45 F 170 N 0.0 Up 0
## 8 54 M 142 N 0.0 Up 0
## 9 37 M 130 Y 1.5 Flat 1
## 10 48 F 120 N 0.0 Up 0
## 11 37 F 142 N 0.0 Up 0
## 12 58 M 99 Y 2.0 Flat 1
## 13 39 M 145 N 0.0 Up 0
## 14 49 M 140 Y 1.0 Flat 1
## 15 42 F 137 N 0.0 Up 0
## 16 54 F 150 N 1.5 Flat 0
## 17 38 M 166 N 0.0 Flat 1
## 18 43 F 165 N 0.0 Up 0
## 19 60 M 125 N 1.0 Flat 1
## 20 36 M 160 N 3.0 Flat 1
## 21 43 F 142 N 0.0 Up 0
## 22 44 M 142 N 1.0 Flat 0
## 23 49 F 164 N 0.0 Up 0
## 24 44 M 150 Y 3.0 Flat 1
## 25 40 M 138 N 0.0 Up 0
## 26 36 M 178 N 0.0 Up 0
## 27 53 M 112 Y 3.0 Flat 0
## 28 52 M 118 N 0.0 Up 0
## 29 53 F 127 N 0.0 Up 0
## 30 51 M 145 N 0.0 Up 0
## 31 53 M 130 N 0.0 Flat 1
## 32 56 M 114 N 0.0 Up 0
## 33 54 M 122 N 2.0 Flat 1
## 34 41 M 130 N 2.0 Flat 1
## 35 43 F 154 N 0.0 Up 0
## 36 32 M 155 N 0.0 Up 0
## 37 65 M 87 Y 1.5 Flat 1
## 38 41 F 142 N 0.0 Up 0
## 39 48 F 148 N 0.0 Up 0
## 40 48 F 130 Y 1.0 Flat 0
## 41 54 F 130 N 0.0 Up 0
## 42 54 F 100 Y 0.0 Flat 1
## 43 35 M 168 N 0.0 Up 0
## 44 52 M 170 N 0.0 Up 0
## 45 43 M 120 Y 1.0 Flat 1
## 46 59 M 120 Y 1.0 Flat 0
## 47 37 M 168 N 0.0 Up 0
## 48 50 M 170 N 0.0 Up 0
## 49 36 M 184 N 1.0 Flat 0
## 50 41 M 170 N 0.0 Flat 1
## 51 50 M 121 Y 2.0 Flat 1
## 52 47 F 98 Y 2.0 Flat 1
## 53 45 M 122 N 0.0 Up 0
## 54 41 F 150 N 0.0 Up 0
## 55 52 F 140 Y 1.5 Flat 0
## 56 51 F 170 N 0.0 Up 0
## 57 31 M 153 Y 1.5 Flat 1
## 58 58 M 140 N 0.0 Flat 1
## 59 54 M 134 N 1.0 Up 0
## 60 52 M 96 Y 1.0 Flat 1
## 61 49 M 174 N 0.0 Up 0
## 62 43 F 175 N 0.0 Up 0
## 63 45 M 144 N 0.0 Up 0
## 64 46 M 125 Y 1.0 Flat 1
## 65 50 F 145 N 0.0 Up 0
## 66 37 F 130 N 0.0 Up 0
## 67 45 F 144 N 0.0 Up 0
## 68 32 M 184 N 0.0 Up 0
## 69 52 M 82 Y 4.0 Flat 1
## 70 44 M 170 N 0.0 Up 0
## 71 57 M 145 Y 1.0 Flat 1
## 72 44 M 135 N 0.0 Up 0
## 73 52 M 150 N 0.0 Flat 1
## 74 44 F 115 N 0.0 Up 0
## 75 55 M 128 Y 1.5 Flat 1
## 76 46 M 116 N 0.0 Up 0
## 77 32 M 130 N 0.0 Flat 1
## 78 35 F 150 N 0.0 Up 0
## 79 52 M 138 Y 0.0 Up 0
## 80 49 M 170 N 0.0 Flat 1
## 81 55 M 160 N 0.0 Up 0
## 82 54 M 154 N 0.0 Up 0
## 83 63 M 115 N 0.0 Flat 1
## 84 52 M 165 N 0.0 Up 0
## 85 56 M 125 Y 1.0 Flat 1
## 86 66 M 94 Y 1.0 Flat 1
## 87 65 M 112 Y 2.0 Flat 1
## 88 53 F 142 Y 2.0 Flat 0
## 89 43 M 155 N 0.0 Flat 1
## 90 55 M 110 Y 0.5 Flat 0
## 91 49 F 160 N 0.0 Up 0
## 92 39 M 140 N 0.0 Up 0
## 93 52 F 148 N 0.0 Up 0
## 94 48 M 92 Y 1.5 Flat 1
## 95 39 F 180 N 0.0 Up 0
## 96 58 M 140 Y 2.0 Flat 1
## 97 43 M 138 N 0.0 Up 0
## 98 39 M 160 N 0.0 Up 0
## 99 56 M 140 N 0.0 Up 0
## 100 41 M 144 N 0.0 Up 0
## 101 65 M 115 Y 1.0 Flat 1
## 102 51 M 100 N 0.0 Up 0
## 103 40 F 130 N 2.0 Flat 1
## 104 40 M 152 Y 1.0 Flat 1
## 105 46 M 124 N 0.0 Flat 1
## 106 57 M 140 N 0.0 Up 0
## 107 48 F 110 N 0.0 Up 0
## 108 34 M 168 N 0.0 Up 0
## 109 50 M 135 N 0.0 Up 0
## 110 39 M 106 N 0.0 Up 0
## 111 59 F 124 N 1.0 Flat 0
## 112 57 M 92 Y 3.0 Flat 1
## 113 47 M 125 Y 0.0 Up 0
## 114 38 M 150 N 0.0 Up 0
## 115 49 F 135 N 0.0 Up 0
## 116 33 F 150 Y 1.0 Flat 1
## 117 38 M 170 N 0.0 Flat 1
## 118 59 F 130 Y 1.5 Flat 1
## 119 35 F 185 N 0.0 Up 0
## 120 34 M 180 N 0.0 Flat 1
## 121 47 F 170 N 0.0 Flat 1
## 122 52 F 139 N 0.0 Up 0
## 123 46 M 140 N 0.0 Up 0
## 124 58 F 110 Y 1.0 Flat 1
## 125 58 M 150 N 0.0 Up 0
## 126 54 M 110 N 0.0 Up 0
## 127 34 F 190 N 0.0 Up 0
## 128 48 F 175 N 2.0 Up 0
## 129 54 F 140 N 0.0 Up 0
## 130 42 M 152 Y 1.5 Flat 0
## 131 38 M 130 N 0.0 Up 0
## 132 46 M 150 Y 0.0 Flat 1
## 133 56 M 122 Y 2.0 Flat 1
## 134 56 M 124 Y 1.5 Flat 1
## 135 61 F 120 Y 1.0 Flat 0
## 136 49 M 175 N 0.0 Flat 1
## 137 43 F 175 N 0.0 Up 0
## 138 39 M 146 N 2.0 Up 0
## 139 54 M 118 Y 0.0 Flat 1
## 140 43 M 130 Y 2.0 Flat 1
## 141 52 M 94 Y 2.5 Flat 1
## 142 50 M 125 Y 2.5 Flat 1
## 143 47 M 158 Y 3.0 Flat 1
## 144 53 M 155 N 0.0 Up 0
## 145 56 F 150 N 1.0 Flat 1
## 146 39 M 132 N 0.0 Up 0
## 147 42 M 155 N 0.0 Up 0
## 148 43 F 176 N 0.0 Up 0
## 149 50 M 160 N 0.0 Up 0
## 150 54 M 125 Y 1.0 Flat 1
## 151 39 M 120 N 0.0 Up 0
## 152 48 M 100 N 0.0 Up 0
## 153 40 M 150 N 0.0 Up 0
## 154 55 M 140 N 0.0 Up 0
## 155 41 M 160 N 0.0 Up 0
## 156 56 M 150 Y 3.0 Flat 1
## 157 38 M 150 Y 1.0 Flat 1
## 158 49 M 130 N 0.0 Up 0
## 159 44 M 100 Y 2.0 Flat 1
## 160 54 M 130 N 1.0 Up 0
## 161 59 M 119 Y 0.0 Flat 1
## 162 49 M 96 Y 0.0 Flat 1
## 163 47 M 174 N 0.0 Up 0
## 164 42 M 150 N 0.0 Up 0
## 165 52 F 140 N 0.0 Up 0
## 166 46 M 175 N 2.0 Flat 1
## 167 50 M 140 Y 5.0 Flat 1
## 168 48 M 118 N 0.0 Up 0
## 169 58 M 100 N 0.0 Up 0
## 170 58 M 160 N 0.0 Up 0
## 171 29 M 160 N 0.0 Up 0
## 172 40 M 188 N 0.0 Up 0
## 173 53 M 162 N 0.0 Up 0
## 174 49 M 172 N 0.0 Up 0
## 175 52 M 134 Y 2.0 Flat 1
## 176 43 M 135 Y 2.0 Flat 1
## 177 54 M 105 N 1.5 Flat 1
## 178 59 M 150 N 0.0 Up 0
## 179 37 M 150 N 0.0 Up 0
## 180 46 F 90 N 0.0 Up 0
## 181 52 M 120 Y 2.0 Flat 1
## 182 51 M 150 N 0.0 Up 0
## 183 52 M 124 Y 2.0 Flat 1
## 184 46 M 140 Y 1.0 Flat 0
## 185 54 F 130 N 0.0 Up 0
## 186 58 M 92 N 0.0 Flat 1
## 187 58 M 110 N 0.0 Up 0
## 188 41 M 138 Y 1.0 Flat 1
## 189 50 F 110 Y 1.0 Flat 0
## 190 53 M 120 Y 1.5 Flat 1
## 191 46 M 120 N 0.0 Up 0
## 192 50 M 116 N 0.0 Up 0
## 193 48 M 160 N 0.0 Up 0
## 194 45 M 110 N 0.0 Up 0
## 195 41 F 180 N 0.0 Up 0
## 196 62 F 116 N 0.0 Up 0
## 197 49 M 132 N 1.0 Flat 0
## 198 42 M 136 N 0.0 Up 0
## 199 53 M 116 Y 0.0 Flat 1
## 200 57 F 98 N 1.0 Flat 0
## 201 47 M 150 N 0.0 Up 0
## 202 46 M 150 N 0.0 Up 0
## 203 42 M 146 N 0.0 Up 0
## 204 31 F 150 N 0.0 Up 0
## 205 56 M 100 N 0.0 Up 0
## 206 50 M 140 Y 0.0 Up 0
## 207 35 M 180 N 0.0 Up 0
## 208 35 M 140 N 0.0 Flat 1
## 209 28 M 185 N 0.0 Up 0
## 210 54 M 140 N 0.0 Flat 1
## 211 48 M 110 N 0.0 Flat 1
## 212 50 F 140 Y 0.0 Flat 1
## 213 56 M 128 Y 1.0 Up 0
## 214 56 F 164 N 0.0 Up 0
## 215 47 M 98 Y 1.5 Flat 1
## 216 30 F 170 N 0.0 Up 0
## 217 39 M 150 N 0.0 Flat 1
## 218 54 M 137 N 0.0 Up 0
## 219 55 M 150 N 0.0 Up 0
## 220 29 M 170 N 0.0 Up 0
## 221 46 M 112 N 0.0 Flat 1
## 222 51 F 150 Y 1.0 Flat 1
## 223 48 F 125 N 0.0 Up 0
## 224 33 M 185 N 0.0 Up 0
## 225 55 M 137 N 0.0 Up 0
## 226 50 M 150 N 0.0 Flat 1
## 227 53 M 140 N 0.0 Up 0
## 228 38 M 134 Y 2.5 Flat 1
## 229 41 M 170 N 0.0 Up 0
## 230 37 F 184 N 0.0 Up 0
## 231 37 M 158 N 0.0 Up 0
## 232 40 M 167 N 0.0 Up 0
## 233 38 F 129 N 0.0 Up 0
## 234 41 M 142 N 0.0 Up 0
## 235 54 F 140 N 0.0 Up 0
## 236 39 M 160 Y 1.0 Flat 0
## 237 41 M 118 Y 3.0 Flat 1
## 238 55 M 136 N 0.0 Flat 1
## 239 48 M 99 Y 2.0 Flat 1
## 240 48 M 102 Y 3.0 Flat 1
## 241 55 M 155 N 0.0 Up 0
## 242 54 M 142 Y 2.0 Flat 1
## 243 55 M 143 Y 2.0 Flat 1
## 244 43 F 118 N 0.0 Up 0
## 245 48 M 103 Y 1.0 Flat 1
## 246 54 M 137 N 2.0 Up 0
## 247 54 M 150 Y 1.5 Flat 1
## 248 48 M 150 Y 2.0 Down 1
## 249 45 M 130 Y 1.0 Flat 1
## 250 49 M 120 Y 1.0 Flat 1
## 251 44 M 135 N 0.0 Flat 1
## 252 48 M 115 N 2.0 Flat 1
## 253 61 M 115 Y 0.0 Up 0
## 254 62 M 152 N 1.0 Up 0
## 255 55 M 96 Y 2.0 Flat 1
## 256 53 F 130 N 0.0 Up 0
## 257 55 F 150 N 0.0 Up 0
## 258 36 M 172 N 0.0 Up 0
## 259 51 F 120 N 0.5 Up 0
## 260 55 F 155 N 0.0 Up 0
## 261 46 M 165 Y 0.0 Up 0
## 262 54 F 138 N 1.0 Up 0
## 263 46 M 115 Y 0.0 Flat 1
## 264 59 M 125 N 0.0 Flat 1
## 265 47 M 145 Y 1.0 Flat 1
## 266 54 M 175 N 0.0 Up 0
## 267 52 M 110 Y 1.0 Flat 1
## 268 34 M 150 N 0.0 Up 0
## 269 54 M 91 Y 1.0 Flat 1
## 270 47 F 145 N 2.0 Flat 0
## 271 45 M 140 N 0.0 Up 0
## 272 32 F 165 N 0.0 Up 0
## 273 55 M 130 Y 3.0 Flat 1
## 274 55 M 134 N 0.0 Up 0
## 275 45 F 180 N 0.0 Up 0
## 276 59 M 100 N 0.0 Up 0
## 277 51 M 150 N 2.0 Flat 1
## 278 52 M 126 Y 1.5 Flat 1
## 279 57 F 126 Y 0.8 Flat 0
## 280 54 F 155 N 0.0 Up 0
## 281 60 M 135 N 0.0 Up 0
## 282 49 M 122 N 2.0 Flat 1
## 283 51 F 160 Y 2.0 Up 0
## 284 55 F 160 N 0.0 Up 0
## 285 42 M 170 N 0.0 Up 0
## 286 51 F 120 N 0.0 Up 0
## 287 59 M 140 N 0.0 Up 0
## 288 53 M 132 N 0.0 Up 0
## 289 48 F 156 N 2.0 Up 0
## 290 36 M 180 N 0.0 Up 0
## 291 48 M 138 N 0.0 Up 0
## 292 47 F 135 N 1.0 Up 0
## 293 53 M 148 N 0.0 Up 0
## 294 65 M 93 Y 0.0 Flat 1
## 295 32 M 127 N 0.7 Up 1
## 296 61 M 110 Y 1.5 Up 1
## 297 50 M 139 Y 0.7 Flat 1
## 298 57 M 131 Y 1.4 Up 1
## 299 51 M 92 N 0.0 Flat 1
## 300 47 M 149 N 2.1 Up 1
## 301 60 M 149 N 0.4 Flat 1
## 302 55 M 150 N 0.2 Up 0
## 303 53 M 120 N 1.5 Up 1
## 304 62 F 123 Y 1.7 Down 1
## 305 51 M 126 N 2.2 Flat 1
## 306 51 F 127 Y 1.5 Up 1
## 307 55 M 155 N 0.1 Flat 1
## 308 53 M 120 N 0.7 Down 0
## 309 58 M 138 N 0.5 Up 1
## 310 57 M 182 N 0.7 Down 1
## 311 65 M 154 N 1.0 Up 0
## 312 60 M 110 N 0.1 Up 1
## 313 41 M 176 N 1.6 Up 1
## 314 34 M 154 N 0.2 Up 1
## 315 53 M 141 Y 2.0 Down 0
## 316 74 M 123 N 1.3 Up 1
## 317 57 M 148 N 0.3 Flat 1
## 318 56 M 121 Y 1.8 Up 1
## 319 61 M 77 N 2.5 Flat 1
## 320 68 M 136 N 1.8 Up 1
## 321 59 M 175 N 2.6 Flat 1
## 322 63 M 109 N -0.9 Flat 1
## 323 38 F 166 N 2.8 Up 1
## 324 62 M 128 Y 2.5 Down 1
## 325 46 M 133 N -2.6 Flat 1
## 326 42 M 128 Y -1.5 Down 1
## 327 45 M 138 N -0.1 Up 0
## 328 59 M 119 Y 0.9 Up 1
## 329 52 M 82 Y 0.8 Flat 1
## 330 60 M 130 Y 1.1 Down 1
## 331 60 M 143 N 2.4 Up 1
## 332 56 M 82 N -1.0 Up 1
## 333 38 M 179 N -1.1 Up 0
## 334 40 M 144 N 0.0 Up 1
## 335 51 M 170 N -0.7 Up 1
## 336 62 M 134 N -0.8 Flat 1
## 337 72 M 114 N 1.6 Flat 0
## 338 63 M 154 N 3.7 Up 1
## 339 63 M 149 N 2.0 Up 1
## 340 64 F 145 N 1.1 Down 1
## 341 43 M 122 N 1.5 Down 1
## 342 64 M 114 Y 1.3 Down 1
## 343 61 M 113 N 1.4 Flat 1
## 344 52 M 120 N 0.0 Flat 1
## 345 51 M 104 N 0.0 Flat 1
## 346 69 M 130 N 0.0 Flat 1
## 347 59 M 115 N 0.0 Flat 1
## 348 48 M 128 N 0.0 Flat 1
## 349 69 M 104 Y 1.6 Flat 1
## 350 36 M 125 Y 1.0 Flat 1
## 351 53 M 120 N 0.0 Flat 1
## 352 43 M 140 Y 0.5 Up 1
## 353 56 M 100 Y -1.0 Down 1
## 354 58 M 100 Y 1.0 Flat 1
## 355 55 M 92 N 0.3 Up 1
## 356 67 M 125 N 0.0 Flat 1
## 357 46 M 113 Y 1.5 Flat 1
## 358 53 M 95 N 0.0 Flat 1
## 359 38 M 128 Y 0.0 Flat 1
## 360 53 M 115 N 0.0 Flat 1
## 361 62 M 72 Y 0.0 Flat 1
## 362 47 M 124 Y 0.0 Flat 1
## 363 56 M 99 N 0.0 Flat 1
## 364 56 M 148 N 0.0 Flat 1
## 365 56 M 97 N 0.0 Flat 0
## 366 64 F 140 Y 1.0 Flat 1
## 367 61 M 117 Y 2.0 Flat 1
## 368 68 M 120 Y 0.0 Up 1
## 369 57 M 120 Y 2.0 Flat 1
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## 371 60 M 63 Y 0.5 Up 1
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## 374 59 M 115 Y 1.0 Flat 1
## 375 61 M 105 Y 0.0 Down 1
## 376 73 F 121 N 0.0 Up 1
## 377 47 M 118 Y 1.0 Flat 1
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## 380 50 M 156 Y 0.0 Up 1
## 381 60 M 99 Y 0.5 Flat 1
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## 383 43 M 145 Y 2.0 Flat 1
## 384 38 F 156 N 0.0 Flat 1
## 385 54 M 155 N 0.0 Flat 1
## 386 61 M 105 Y 0.0 Flat 1
## 387 42 M 99 Y 0.0 Flat 1
## 388 53 M 135 Y 1.0 Flat 1
## 389 55 M 83 N 0.0 Flat 1
## 390 61 M 145 N 1.0 Flat 1
## 391 51 M 60 N 0.0 Flat 1
## 392 70 M 92 Y 0.0 Flat 1
## 393 61 M 115 N 0.0 Flat 1
## 394 38 M 120 Y 0.7 Flat 1
## 395 57 M 98 Y 2.0 Flat 1
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## 397 62 F 143 N 0.0 Flat 1
## 398 58 M 105 Y 0.0 Flat 1
## 399 52 M 122 Y 1.0 Up 1
## 400 61 M 70 N 0.0 Flat 1
## 401 50 F 110 N 0.0 Flat 1
## 402 51 M 163 N 0.0 Flat 1
## 403 65 M 67 N 0.7 Flat 1
## 404 52 M 128 Y 2.0 Flat 1
## 405 47 M 120 Y 0.0 Flat 1
## 406 35 M 130 Y 1.2 Flat 1
## 407 57 M 100 Y 0.0 Flat 1
## 408 62 M 72 Y -0.5 Flat 1
## 409 59 M 94 N 0.0 Flat 1
## 410 53 M 122 Y 0.0 Flat 1
## 411 62 M 78 N 2.0 Flat 1
## 412 54 M 150 N 1.5 Flat 1
## 413 56 M 103 Y 1.0 Flat 1
## 414 56 M 98 N -2.0 Flat 1
## 415 54 M 110 Y 3.0 Flat 1
## 416 66 F 90 N 0.0 Flat 1
## 417 63 M 112 Y 3.0 Flat 1
## 418 44 M 127 N 0.0 Up 0
## 419 60 M 140 Y 1.5 Down 1
## 420 55 M 149 Y 2.5 Up 1
## 421 66 M 99 Y 1.3 Flat 0
## 422 66 M 120 N -0.5 Up 0
## 423 65 M 105 Y 0.0 Flat 1
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## 425 60 M 141 Y 2.0 Up 1
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## 451 52 M 110 Y 2.0 Down 1
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## 475 62 M 130 N 0.1 Up 0
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## 477 51 M 139 N 0.1 Up 0
## 478 61 M 108 Y 2.0 Down 1
## 479 57 M 148 Y 2.0 Flat 1
## 480 56 M 123 Y 2.5 Flat 1
## 481 58 M 110 Y 2.0 Flat 1
## 482 69 M 118 N 2.5 Down 1
## 483 67 M 125 N 2.5 Up 1
## 484 58 M 106 Y 1.5 Down 1
## 485 65 M 112 Y 1.1 Flat 1
## 486 63 M 128 Y 1.2 Flat 1
## 487 55 M 180 N 0.4 Up 0
## 488 57 M 144 Y 2.0 Flat 1
## 489 65 M 135 N 0.3 Up 0
## 490 54 M 140 Y 3.0 Flat 1
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## 497 58 M 69 N 1.0 Down 0
## 498 61 M 148 Y 3.0 Down 1
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## 500 62 M 130 Y 1.0 Flat 1
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## 504 51 M 138 N 0.2 Up 0
## 505 62 M 112 Y 3.0 Down 1
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## 511 60 M 126 N 0.3 Up 0
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## 518 65 M 120 Y 1.5 Flat 1
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## 528 61 M 135 N 0.3 Up 0
## 529 49 M 127 Y 1.5 Flat 1
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## 918 38 M 173 N 0.0 Up 0#Imputation
heart$Age[is.na(heart$Age)] <-mean(heart$Age, na.rm = T)
sapply(heart, function(x)sum(is.na(x)))## Age Sex MaxHR ExerciseAngina Oldpeak
## 0 0 0 0 0
## ST_Slope HeartDisease
## 0 0heart1 <- heart[1:800,]
validation <- heart[801:889,]model <- glm(HeartDisease ~ ., family = binomial(link = "logit"), data = heart1)
summary(model)##
## Call:
## glm(formula = HeartDisease ~ ., family = binomial(link = "logit"),
## data = heart1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7175 -0.5577 0.2662 0.5009 2.6022
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.27927 1.18865 -1.076 0.28182
## Age 0.02237 0.01248 1.793 0.07293 .
## SexM 1.71848 0.27800 6.182 6.35e-10 ***
## MaxHR -0.01230 0.00473 -2.599 0.00935 **
## ExerciseAnginaY 1.01656 0.23714 4.287 1.81e-05 ***
## Oldpeak 0.37655 0.12276 3.067 0.00216 **
## ST_SlopeFlat 1.27289 0.41038 3.102 0.00192 **
## ST_SlopeUp -1.35888 0.42123 -3.226 0.00126 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1094.95 on 799 degrees of freedom
## Residual deviance: 596.23 on 792 degrees of freedom
## AIC: 612.23
##
## Number of Fisher Scoring iterations: 5#Evaluate Model
library(ROCR)
predictionLogistic <- predict(model, validation, type= "response")
pd <- prediction(predictionLogistic, validation$HeartDisease)
rocCurve <- performance(pd, measure = "tpr", x.measure = "fpr")
plot(rocCurve)
auc <- performance(pd, measure = "auc")
auc <- auc@y.values[[1]]
auc## [1] 0.8586171#check accuracy
result <- ifelse(predictionLogistic > 0.5, 1, 0)
misclassificationError <- mean(result != validation$HeartDisease)
print(paste("Accuracy: " , 1-misclassificationError))## [1] "Accuracy: 0.764044943820225"