knitr::opts_chunk$set(echo = TRUE)
library(readxl)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.2.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
library(gtools)
library(gmodels)
## Warning: package 'gmodels' was built under R version 4.2.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.2.3
library(class)
library(tidyr)
## Warning: package 'tidyr' was built under R version 4.2.3
Memanggil Data dan Membentuk Data Frame
dt <- read_excel("D:/SEM 6/BISMILLAH LOMBA/SATDAT JUARA PART 2/Data fix/Data Cleaning_Hanung Safrizal.xlsx",
sheet = "Data modif")
head(dt)
## # A tibble: 6 × 16
## Target_PJK Age Sex GD_Puasa GD_PP Kolestrol LDL HDL Lipoprotein
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 57 1 94 131 212 144 48 85
## 2 0 57 2 112 242 266 159 64 95
## 3 0 49 1 87 55 209 146 38 81
## 4 0 46 2 99 106 227 137 57 91
## 5 0 53 1 87 117 217 119 84 91
## 6 0 32 1 84 85 174 103 46 70.8
## # ℹ 7 more variables: Berat_Badan <dbl>, Target_Hipertensi <dbl>,
## # Nyeri_Dada <dbl>, Riwayat_Keluarga <dbl>, Sistolik <dbl>, Diastolik <dbl>,
## # Merokok <dbl>
str(dt)
## tibble [5,327 × 16] (S3: tbl_df/tbl/data.frame)
## $ Target_PJK : num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
## $ Age : num [1:5327] 57 57 49 46 53 32 27 40 35 38 ...
## $ Sex : num [1:5327] 1 2 1 2 1 1 1 2 1 2 ...
## $ GD_Puasa : num [1:5327] 94 112 87 99 87 84 79 82 89 86 ...
## $ GD_PP : num [1:5327] 131 242 55 106 117 85 68 121 109 89 ...
## $ Kolestrol : num [1:5327] 212 266 209 227 217 174 159 170 184 119 ...
## $ LDL : num [1:5327] 144 159 146 137 119 103 101 107 124 76 ...
## $ HDL : num [1:5327] 48 64 38 57 84 46 50 38 52 51 ...
## $ Lipoprotein : num [1:5327] 85 95 81 91 91 ...
## $ Berat_Badan : num [1:5327] 70 61.4 53.3 66.7 67 ...
## $ Target_Hipertensi: num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
## $ Nyeri_Dada : num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
## $ Riwayat_Keluarga : num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
## $ Sistolik : num [1:5327] 100 118 86 120 126 108 100 102 82 114 ...
## $ Diastolik : num [1:5327] 66 63 57 83 69 67 62 80 77 70 ...
## $ Merokok : num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
hist(dt$Target_PJK)
# Create a contingency table of counts
count_data <- table(dt$Merokok, dt$Target_PJK)
# Define colors for each category
colors <- c("lightblue", "lightgreen", "pink", "orange", "yellow")
# Create stacked bar plot
barplot(count_data, beside = TRUE, legend = TRUE, col = colors,
xlab = "Target_PJK", ylab = "Count", main = "Hubungan antara Merokok dan Target_PJK",
args.legend = list(x = "topright", bty = "n", title = "Merokok"))
# Add legend
legend("topright", legend = c("Merokok 0", "Merokok 1", "Merokok 2", "Merokok 3", "Merokok 4"),
fill = colors)
# Create a contingency table of counts
count_data <- table(dt$Merokok, dt$Target_PJK)
# Calculate the proportions for each category
prop_data <- prop.table(count_data, margin = 1) * 100
# Define colors for each category
colors <- c("lightblue", "lightgreen", "pink", "orange", "yellow")
# Create stacked bar plot with percentages
barplot(prop_data, beside = TRUE, legend = TRUE, col = colors,
xlab = "Target_PJK", ylab = "Percentage", main = "Hubungan antara Merokok dan Target_PJK",
args.legend = list(x = "topright", bty = "n", title = "Merokok"))
# Add legend
legend("topright", legend = c("Merokok 0", "Merokok 1", "Merokok 2", "Merokok 3", "Merokok 4"),
fill = colors)
# Subset data for Target_PJK = 0
subset_data <- subset(dt, Target_PJK == 0)
# Calculate the percentage for each category
percentages <- prop.table(table(subset_data$Merokok)) * 100
# Create bar plot for percentages
barplot(percentages, col = c("lightblue", "lightgreen", "pink", "orange"),
xlab = "Merokok", ylab = "Percentage", main = "Persentase Merokok pada Target_PJK = 0",
ylim = c(0, max(percentages) + 10))
# Add text labels for percentages
text(x = 1:length(percentages), y = percentages, labels = paste0(round(percentages, 1), "%"), pos = 3)
# Subset data for Target_PJK = 0
subset_data <- subset(dt, Target_PJK == 1)
# Calculate the percentage for each category
percentages <- prop.table(table(subset_data$Merokok)) * 100
# Create bar plot for percentages
barplot(percentages, col = c("lightblue", "lightgreen", "pink", "orange"),
xlab = "Merokok", ylab = "Percentage", main = "Persentase Merokok pada Target_PJK = 1",
ylim = c(0, max(percentages) + 10))
# Add text labels for percentages
text(x = 1:length(percentages), y = percentages, labels = paste0(round(percentages, 1), "%"), pos = 3)
str(dt$Target_PJK)
## num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
str(dt$Merokok)
## num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
# Pastikan tidak ada nilai NA di setiap kolom
dt <- na.omit(dt)
# Membentuk Data Frame
dt$Sex <- as.factor(dt$Sex)
dt$Target_PJK <- as.factor(dt$Target_PJK)
dt$Target_Hipertensi<- as.factor(dt$Target_Hipertensi)
dt$Riwayat_Keluarga<- as.factor(dt$Riwayat_Keluarga)
# Periksa hasil
summary(dt)
## Target_PJK Age Sex GD_Puasa GD_PP
## 0:4855 Min. :22.00 1:1787 Min. : 49.00 Min. : 28.0
## 1: 472 1st Qu.:35.00 2:3540 1st Qu.: 77.00 1st Qu.: 91.0
## Median :44.00 Median : 83.00 Median :107.0
## Mean :43.85 Mean : 89.36 Mean :121.4
## 3rd Qu.:52.00 3rd Qu.: 90.25 3rd Qu.:131.0
## Max. :99.00 Max. :665.00 Max. :677.0
## Kolestrol LDL HDL Lipoprotein
## Min. : 78.0 Min. : 0.0 Min. : 10.00 Min. : 44.00
## 1st Qu.:165.5 1st Qu.: 97.0 1st Qu.: 46.00 1st Qu.: 70.70
## Median :188.0 Median :116.0 Median : 53.00 Median : 80.00
## Mean :191.6 Mean :119.2 Mean : 54.87 Mean : 79.87
## 3rd Qu.:213.0 3rd Qu.:137.5 3rd Qu.: 61.00 3rd Qu.: 88.00
## Max. :474.0 Max. :373.0 Max. :3985.00 Max. :169.00
## Berat_Badan Target_Hipertensi Nyeri_Dada Riwayat_Keluarga
## Min. : 0.0 0:4700 Min. :0.00000 0:5126
## 1st Qu.: 49.8 1: 627 1st Qu.:0.00000 1: 201
## Median : 56.5 Median :0.00000
## Mean : 57.8 Mean :0.06908
## 3rd Qu.: 64.8 3rd Qu.:0.00000
## Max. :123.3 Max. :1.00000
## Sistolik Diastolik Merokok
## Min. : 58.0 Min. : 35.00 Min. :0.0000
## 1st Qu.: 97.0 1st Qu.: 62.00 1st Qu.:0.0000
## Median :106.0 Median : 69.00 Median :0.0000
## Mean :110.9 Mean : 71.01 Mean :0.2399
## 3rd Qu.:118.0 3rd Qu.: 77.00 3rd Qu.:0.0000
## Max. :269.0 Max. :169.00 Max. :1.0000
# Menampilkan struktur data frame
str(dt)
## tibble [5,327 × 16] (S3: tbl_df/tbl/data.frame)
## $ Target_PJK : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : num [1:5327] 57 57 49 46 53 32 27 40 35 38 ...
## $ Sex : Factor w/ 2 levels "1","2": 1 2 1 2 1 1 1 2 1 2 ...
## $ GD_Puasa : num [1:5327] 94 112 87 99 87 84 79 82 89 86 ...
## $ GD_PP : num [1:5327] 131 242 55 106 117 85 68 121 109 89 ...
## $ Kolestrol : num [1:5327] 212 266 209 227 217 174 159 170 184 119 ...
## $ LDL : num [1:5327] 144 159 146 137 119 103 101 107 124 76 ...
## $ HDL : num [1:5327] 48 64 38 57 84 46 50 38 52 51 ...
## $ Lipoprotein : num [1:5327] 85 95 81 91 91 ...
## $ Berat_Badan : num [1:5327] 70 61.4 53.3 66.7 67 ...
## $ Target_Hipertensi: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Nyeri_Dada : num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
## $ Riwayat_Keluarga : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Sistolik : num [1:5327] 100 118 86 120 126 108 100 102 82 114 ...
## $ Diastolik : num [1:5327] 66 63 57 83 69 67 62 80 77 70 ...
## $ Merokok : num [1:5327] 0 0 0 0 0 0 0 0 0 0 ...
Data Proportion
pcheart <- table(dt$Target_PJK)
pcheart
##
## 0 1
## 4855 472
prop.table(table(dt$Target_PJK))
##
## 0 1
## 0.91139478 0.08860522
Splitting Data
library(caret)
## Loading required package: lattice
set.seed(45) # digunakan untuk menghasilkan hasil acak yang sama setiap kali dijalankan
train_index <- createDataPartition(y = dt$Target_PJK, p = .7, list = FALSE) # memilih indeks secara acak untuk set pelatihan
train_dt <- as.data.frame(dt[train_index, ]) # memilih data untuk set pelatihan
test_dt <- as.data.frame(dt[-train_index, ]) # memilih data untuk set pengujian
table(train_dt$Target_PJK)
##
## 0 1
## 3399 331
table(test_dt$Target_PJK)
##
## 0 1
## 1456 141
Balancing Data
Smote
library(UBL)
## Warning: package 'UBL' was built under R version 4.2.3
## Loading required package: MBA
## Warning: package 'MBA' was built under R version 4.2.3
## Loading required package: gstat
## Warning: package 'gstat' was built under R version 4.2.3
## Loading required package: automap
## Warning: package 'automap' was built under R version 4.2.3
## Loading required package: sp
## Warning: package 'sp' was built under R version 4.2.3
## Loading required package: randomForest
## Warning: package 'randomForest' was built under R version 4.2.3
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
##
## margin
## The following object is masked from 'package:dplyr':
##
## combine
set.seed(123)
smote_reglog <- SmoteClassif(form = Target_PJK ~ ., dat = train_dt, C.perc = list("1"=1500/331,"0"=1500/3398), dist = "HVDM")
prop_table <- prop.table(table(smote_reglog$Target_PJK))
print(prop_table)
##
## 0 1
## 0.5 0.5
table(smote_reglog$Target_PJK)
##
## 0 1
## 1500 1500
ROSE
library(ROSE)
## Warning: package 'ROSE' was built under R version 4.2.3
## Loaded ROSE 0.0-4
oversampling_reglog <- ROSE(Target_PJK ~ ., data = train_dt, seed = 123)$data
# Compute class proportions
prop_table <- prop.table(table(oversampling_reglog$Target_PJK))
print(prop_table)
##
## 0 1
## 0.5029491 0.4970509
table(oversampling_reglog$Target_PJK)
##
## 0 1
## 1876 1854
Undersampling
undersampled_reglog <- ovun.sample(Target_PJK ~ ., data = train_dt, method = "under")$data
# Melihat data yang telah diundersampling
print(undersampled_reglog)
## Target_PJK Age Sex GD_Puasa GD_PP Kolestrol LDL HDL Lipoprotein
## 1 0 46 1 100.0 122.0 191.00 126.00 54.00 77.5
## 2 0 26 1 81.0 107.0 200.00 126.00 67.00 79.0
## 3 0 29 2 82.0 72.0 180.00 129.00 42.00 90.3
## 4 0 54 1 83.0 149.0 255.00 171.00 56.00 88.1
## 5 0 46 2 72.0 106.8 208.90 116.00 47.00 58.0
## 6 0 25 2 70.0 122.0 164.00 92.00 58.00 90.4
## 7 0 26 2 77.0 135.0 243.00 187.00 47.00 88.0
## 8 0 37 2 81.0 84.3 148.00 89.00 49.00 80.0
## 9 0 52 1 73.0 82.0 191.00 135.00 46.00 61.5
## 10 0 45 1 82.0 135.0 179.00 107.00 49.00 92.0
## 11 0 30 1 77.0 97.0 158.00 97.00 41.00 69.0
## 12 0 45 2 76.0 75.4 155.00 81.00 66.00 60.8
## 13 0 29 2 85.0 92.6 202.00 133.00 56.00 65.0
## 14 0 58 2 89.0 96.0 162.00 49.00 89.00 79.5
## 15 0 46 2 78.1 94.0 179.00 114.00 50.00 100.0
## 16 0 32 2 96.0 117.0 253.00 137.00 38.00 80.9
## 17 0 37 2 87.0 134.5 95.00 47.00 52.00 89.0
## 18 0 34 2 86.0 103.0 140.90 88.10 49.00 80.6
## 19 0 33 2 77.0 104.0 199.00 122.00 52.00 79.0
## 20 0 36 2 95.0 75.2 161.50 88.70 39.00 72.5
## 21 0 31 2 85.0 78.0 181.00 110.00 61.00 70.0
## 22 0 51 1 83.0 92.0 192.00 129.00 57.00 70.7
## 23 0 63 2 137.0 252.0 166.00 102.00 38.00 80.5
## 24 0 36 2 95.0 139.0 198.00 124.00 57.00 100.1
## 25 0 28 1 82.0 99.0 187.00 126.00 71.40 99.0
## 26 0 47 1 78.0 88.0 199.00 110.00 63.00 74.5
## 27 0 58 2 94.0 80.0 161.00 97.00 50.00 78.3
## 28 0 53 2 85.0 85.0 227.00 101.00 58.00 82.0
## 29 0 64 1 90.0 115.0 179.00 112.00 55.00 97.0
## 30 0 49 2 86.0 111.0 204.00 116.00 65.00 80.0
## 31 0 54 2 77.0 199.0 303.00 160.00 49.00 101.0
## 32 0 33 2 77.8 60.8 138.40 82.40 67.00 62.5
## 33 0 35 2 57.0 101.0 179.00 116.00 52.00 77.0
## 34 0 46 1 87.0 71.0 151.00 79.00 71.00 72.0
## 35 0 37 2 76.0 124.0 188.00 129.00 50.00 82.0
## 36 0 50 2 85.0 94.0 202.00 112.00 59.00 77.0
## 37 0 39 1 90.0 93.0 184.00 133.00 38.00 77.0
## 38 0 51 2 81.0 113.0 258.00 202.00 56.00 88.0
## 39 0 39 2 82.0 139.0 200.00 124.00 51.00 82.5
## 40 0 45 2 92.0 96.0 161.00 89.00 63.00 94.7
## 41 0 41 2 93.0 109.0 153.00 105.00 57.80 91.0
## 42 0 48 2 90.0 93.0 222.00 118.00 65.00 69.2
## 43 0 30 2 79.8 121.1 127.00 75.00 46.00 78.5
## 44 0 43 2 88.0 97.0 154.00 92.00 57.00 79.0
## 45 0 38 2 82.0 75.2 201.10 131.60 54.00 80.1
## 46 0 51 1 79.0 91.0 119.40 58.30 55.00 69.0
## 47 0 39 2 81.0 145.0 217.00 140.00 70.00 77.0
## 48 0 47 1 69.0 105.0 221.00 129.00 46.00 85.5
## 49 0 41 2 70.0 87.0 166.00 103.00 56.00 85.5
## 50 0 57 2 96.0 150.0 257.00 167.00 49.60 90.1
## 51 0 31 2 89.0 102.0 188.00 109.00 67.00 62.0
## 52 0 33 1 152.0 252.0 149.00 101.00 31.00 93.0
## 53 0 39 2 93.0 82.0 224.00 172.00 40.00 81.5
## 54 0 43 1 75.0 75.0 172.00 105.00 34.00 81.0
## 55 0 51 2 97.0 158.0 230.00 154.00 45.00 60.3
## 56 0 44 1 73.0 83.0 182.00 93.00 75.00 60.6
## 57 0 29 1 74.0 93.0 159.00 92.00 53.00 76.5
## 58 0 53 1 87.0 83.0 173.00 120.00 89.50 56.0
## 59 0 43 2 88.0 96.0 238.00 157.00 68.00 77.0
## 60 0 31 2 80.0 117.0 156.00 83.00 46.00 67.0
## 61 0 58 2 88.0 145.0 294.00 224.00 64.00 113.0
## 62 0 56 2 94.0 126.0 165.00 87.00 67.00 66.0
## 63 0 42 1 90.0 120.0 162.00 91.00 57.00 74.0
## 64 0 26 2 86.0 86.0 146.00 96.00 44.00 64.0
## 65 0 54 2 75.0 91.0 169.00 114.00 63.00 84.0
## 66 0 43 2 88.0 112.0 197.00 106.00 68.00 72.0
## 67 0 57 2 88.0 85.0 206.00 127.00 70.00 87.0
## 68 0 41 1 73.0 134.0 174.00 119.00 45.00 90.6
## 69 0 44 2 79.0 90.0 203.00 93.00 61.00 69.3
## 70 0 36 1 79.0 118.0 215.00 152.00 49.00 79.0
## 71 0 58 1 99.0 173.0 228.00 162.00 52.00 88.0
## 72 0 44 2 83.0 159.0 214.00 121.00 61.00 80.0
## 73 0 36 2 78.0 101.0 180.00 124.00 51.00 79.0
## 74 0 32 2 88.0 131.0 211.80 139.00 56.00 82.2
## 75 0 25 2 85.0 102.4 151.19 90.99 54.42 90.0
## 76 0 34 2 63.0 45.5 193.00 116.30 57.00 60.3
## 77 0 44 2 83.0 103.0 217.00 107.00 42.00 89.0
## 78 0 45 2 103.0 115.0 174.00 92.00 43.00 82.0
## 79 0 33 2 75.0 90.0 178.00 119.00 57.00 84.5
## 80 0 28 1 92.0 203.0 160.00 83.00 36.00 93.0
## 81 0 41 2 85.0 94.0 201.00 135.00 50.00 90.0
## 82 0 31 1 74.0 84.0 187.00 117.00 67.00 80.0
## 83 0 62 2 71.0 104.0 145.00 89.00 48.00 88.6
## 84 0 34 2 81.0 127.0 164.40 103.00 56.00 71.0
## 85 0 35 2 91.0 92.0 202.00 123.00 61.00 75.5
## 86 0 60 1 194.0 259.0 271.00 190.00 43.00 80.2
## 87 0 42 2 108.0 178.0 223.00 150.00 71.00 89.0
## 88 0 47 2 65.0 40.0 199.00 135.00 46.00 79.0
## 89 0 49 2 86.0 90.0 215.00 158.00 48.00 80.0
## 90 0 55 1 78.0 114.0 187.00 93.00 48.00 81.0
## 91 0 36 2 69.0 82.0 167.00 101.00 60.00 78.0
## 92 0 30 1 78.0 127.0 168.00 105.00 50.00 70.2
## 93 0 50 2 118.0 153.0 246.00 168.00 49.00 81.5
## 94 0 29 2 79.0 69.0 204.00 115.00 71.00 70.3
## 95 0 33 2 75.0 88.0 147.00 86.00 58.30 80.8
## 96 0 42 2 82.0 100.0 159.00 65.00 59.00 60.7
## 97 0 26 1 78.0 78.0 121.00 81.00 39.00 60.8
## 98 0 27 2 76.0 95.0 167.00 106.00 58.00 67.0
## 99 0 30 2 88.0 113.7 166.50 100.00 53.80 72.5
## 100 0 51 1 100.0 99.0 185.00 106.00 40.00 76.0
## 101 0 49 2 80.0 151.0 163.00 104.00 47.00 61.0
## 102 0 33 2 68.0 48.0 154.00 88.00 56.00 79.0
## 103 0 38 2 81.0 135.0 198.00 101.00 59.00 80.0
## 104 0 46 2 223.0 297.0 222.00 153.00 47.00 106.0
## 105 0 53 2 77.0 89.0 195.00 111.00 50.00 90.0
## 106 0 37 1 103.0 156.0 229.00 160.00 43.00 80.6
## 107 0 32 2 69.0 69.0 109.00 53.00 45.00 60.3
## 108 0 49 1 80.0 96.0 160.00 101.00 46.00 84.0
## 109 0 41 1 74.0 126.0 183.00 117.00 69.00 80.0
## 110 0 59 2 89.0 185.0 189.00 126.00 43.00 68.0
## 111 0 36 2 95.0 108.0 224.00 146.00 50.20 80.0
## 112 0 44 2 73.0 77.0 142.00 98.00 49.00 92.0
## 113 0 40 1 76.0 97.0 191.00 125.00 40.00 70.0
## 114 0 43 1 76.0 90.0 201.00 117.00 55.00 100.0
## 115 0 54 2 107.0 150.0 277.00 180.00 64.00 65.0
## 116 0 56 2 104.0 191.0 209.00 136.00 54.00 72.5
## 117 0 60 2 62.0 85.0 186.00 106.00 85.00 72.0
## 118 0 41 1 86.0 129.0 192.00 133.00 56.00 106.8
## 119 0 46 2 89.0 84.0 202.00 128.00 49.00 67.0
## 120 0 28 2 82.0 83.0 199.00 137.00 48.50 90.2
## 121 0 42 2 70.0 91.0 158.00 100.00 54.00 74.0
## 122 0 42 1 79.0 133.0 192.00 116.00 70.00 82.0
## 123 0 38 1 93.0 138.0 139.70 78.00 49.00 80.0
## 124 0 48 2 89.0 85.0 211.00 144.00 50.00 75.0
## 125 0 30 2 83.0 109.0 173.00 86.00 77.00 60.0
## 126 0 26 2 87.2 89.0 144.00 90.00 49.00 77.5
## 127 0 58 2 164.0 280.0 179.00 104.00 43.00 83.0
## 128 0 43 2 88.0 109.0 207.00 121.00 56.00 84.5
## 129 0 44 2 72.0 102.0 184.00 134.00 37.00 84.0
## 130 0 50 1 96.0 98.0 184.00 126.00 58.00 72.0
## 131 0 31 2 77.0 128.0 171.00 97.00 49.00 84.5
## 132 0 48 2 102.0 121.0 192.00 86.00 35.00 90.0
## 133 0 33 2 82.0 116.0 170.00 119.00 49.00 71.0
## 134 0 38 2 80.0 38.0 161.00 95.00 52.00 64.0
## 135 0 35 2 82.0 90.0 191.00 105.00 62.00 71.0
## 136 0 42 2 74.0 71.0 152.00 79.00 47.00 67.0
## 137 0 48 2 81.0 117.0 187.00 115.00 66.00 82.3
## 138 0 61 1 93.0 96.0 153.00 67.00 40.20 94.0
## 139 0 42 2 84.0 92.0 204.00 124.00 44.00 78.5
## 140 0 43 2 78.0 114.0 203.00 116.00 85.60 70.0
## 141 0 54 2 77.0 103.0 223.00 162.00 46.00 79.5
## 142 0 53 2 81.8 91.6 202.84 142.17 52.00 60.9
## 143 0 39 2 76.0 85.0 219.00 162.00 51.00 89.0
## 144 0 45 1 87.0 88.0 176.00 113.00 45.00 87.0
## 145 0 43 2 74.0 174.0 190.00 114.00 47.40 83.0
## 146 0 34 2 86.0 105.0 189.00 112.00 67.00 86.5
## 147 0 62 2 99.0 215.0 295.00 181.00 71.00 97.0
## 148 0 60 2 83.0 116.0 251.00 171.00 72.00 82.0
## 149 0 29 2 65.5 77.1 140.00 81.00 48.00 80.1
## 150 0 40 2 90.0 111.0 147.00 88.00 61.00 73.0
## 151 0 36 2 105.0 115.0 249.00 180.00 52.00 102.0
## 152 0 49 2 74.0 87.0 148.00 91.00 62.00 64.0
## 153 0 61 1 81.0 90.0 194.00 117.00 55.00 70.7
## 154 0 50 2 86.0 115.0 190.00 115.00 57.00 89.0
## 155 0 29 1 78.0 98.7 135.20 76.80 66.00 70.0
## 156 0 47 1 156.0 387.0 241.00 163.00 40.00 60.0
## 157 0 64 1 83.0 95.0 188.00 141.00 45.00 80.3
## 158 0 57 2 72.0 80.0 212.00 122.00 71.00 90.0
## 159 0 52 1 91.0 188.0 199.00 135.00 36.00 99.0
## 160 0 56 2 86.0 126.0 224.00 120.00 43.00 88.5
## 161 0 28 2 94.0 107.0 168.00 116.00 45.00 88.0
## 162 0 60 2 64.0 107.0 106.00 70.00 48.00 76.0
## 163 0 28 2 71.0 89.0 170.00 116.00 49.00 60.3
## 164 0 32 2 75.0 123.0 174.00 109.00 57.00 87.0
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## 488 1 52 2 93.0 152.0 242.00 125.00 37.00 97.7
## 489 1 65 2 131.0 287.0 255.00 179.00 49.00 108.0
## 490 1 57 2 377.0 452.0 333.00 192.00 47.00 89.0
## 491 1 56 1 123.0 163.0 146.00 63.00 30.00 89.5
## 492 1 60 2 95.0 161.0 298.00 213.00 44.00 93.5
## 493 1 52 2 365.0 484.0 282.00 177.00 36.00 88.5
## 494 1 44 1 100.0 124.0 252.00 186.00 47.00 93.0
## 495 1 55 2 393.0 653.0 300.00 172.00 39.00 90.0
## 496 1 57 2 103.0 134.0 238.00 143.00 75.00 78.0
## 497 1 54 2 194.0 273.0 209.00 123.00 49.00 117.5
## 498 1 60 2 103.0 136.0 263.00 191.00 48.00 102.0
## 499 1 48 2 94.0 151.0 192.00 135.00 45.00 98.0
## 500 1 61 2 153.0 281.0 258.00 127.00 79.00 169.0
## 501 1 60 2 79.0 132.0 170.00 109.00 52.00 66.5
## 502 1 57 2 157.0 335.0 184.00 117.00 41.00 97.0
## 503 1 44 2 91.0 138.0 241.00 161.00 50.00 89.4
## 504 1 57 1 114.0 218.0 293.00 240.00 44.00 115.5
## 505 1 41 1 95.0 185.0 237.00 189.00 39.00 87.0
## 506 1 51 2 145.0 238.0 200.00 125.00 38.00 100.0
## 507 1 62 2 102.0 152.0 235.00 166.00 35.00 101.0
## 508 1 58 1 130.0 359.0 246.00 157.00 57.00 86.0
## 509 1 57 2 153.0 167.0 264.00 160.00 43.00 96.0
## 510 1 37 1 94.0 172.0 145.00 70.00 47.00 78.0
## 511 1 60 1 109.0 202.0 136.00 62.00 50.00 103.0
## 512 1 62 1 97.0 205.0 239.00 148.00 43.00 85.5
## 513 1 35 2 83.0 107.0 189.00 180.00 54.00 80.0
## 514 1 55 2 205.0 468.0 329.00 134.00 36.00 85.0
## 515 1 55 2 102.0 174.0 204.00 144.00 31.00 119.0
## 516 1 55 2 89.0 103.0 240.00 176.00 50.00 73.0
## 517 1 58 2 85.0 141.0 270.00 176.00 41.00 104.0
## 518 1 62 1 92.0 125.0 252.00 146.00 66.00 97.0
## 519 1 46 1 138.0 274.0 267.00 179.00 57.00 91.0
## 520 1 53 2 101.0 138.0 137.00 120.00 54.00 80.7
## 521 1 42 1 80.0 251.0 204.00 125.00 42.00 82.5
## 522 1 49 2 89.0 162.0 190.00 116.00 44.00 93.0
## 523 1 48 2 93.0 136.0 221.00 151.00 48.00 92.0
## 524 1 50 2 101.0 244.0 212.00 132.00 34.00 93.0
## 525 1 58 2 367.0 496.0 220.00 159.00 40.00 102.0
## 526 1 50 2 91.0 161.0 214.00 121.00 54.00 96.0
## 527 1 60 2 99.0 203.0 224.00 155.00 34.00 94.0
## 528 1 64 2 87.0 164.0 220.00 128.00 23.00 98.0
## 529 1 41 2 86.0 162.0 183.00 117.00 41.00 99.0
## 530 1 34 2 96.0 129.0 181.00 93.00 69.00 65.0
## 531 1 55 2 100.0 162.0 298.00 208.00 60.00 85.0
## 532 1 47 2 89.0 127.0 223.00 142.00 42.00 82.0
## 533 1 40 1 94.0 164.0 208.00 184.00 44.00 106.0
## 534 1 64 1 107.0 198.0 228.00 146.00 55.00 79.0
## 535 1 57 2 94.0 159.0 251.00 189.00 42.00 103.5
## 536 1 39 2 91.0 149.0 187.00 127.00 70.00 75.0
## 537 1 49 1 198.0 315.0 205.00 120.00 41.00 100.0
## 538 1 53 2 99.0 151.0 236.00 211.00 56.00 103.0
## 539 1 51 1 90.0 93.0 216.00 130.00 62.00 83.0
## 540 1 54 2 98.0 144.0 217.00 146.00 50.00 85.0
## 541 1 49 2 101.0 139.0 266.00 190.00 45.00 96.6
## 542 1 62 2 96.0 145.0 228.00 166.00 40.00 81.0
## 543 1 56 2 109.0 232.0 236.00 165.00 50.00 74.5
## 544 1 51 2 120.0 211.0 214.00 143.00 54.00 104.0
## 545 1 46 2 99.0 151.0 200.00 114.00 55.00 96.0
## 546 1 42 2 149.0 316.0 249.00 203.00 38.00 92.0
## 547 1 56 2 96.0 126.0 239.00 170.00 50.00 93.0
## 548 1 47 1 110.0 147.0 255.00 269.00 52.00 89.0
## 549 1 51 2 99.0 151.0 211.00 116.00 69.00 73.0
## 550 1 46 2 99.0 182.0 275.00 242.00 44.00 94.0
## 551 1 59 2 95.0 137.0 244.00 173.00 39.00 98.0
## 552 1 47 1 129.0 245.0 260.00 191.00 36.00 114.0
## 553 1 55 2 89.0 106.0 182.00 103.00 58.00 62.0
## 554 1 60 2 93.0 127.0 185.00 124.00 52.00 79.0
## 555 1 67 2 98.0 145.0 215.00 205.00 39.00 111.0
## 556 1 55 2 98.0 135.0 283.00 266.00 60.00 99.0
## 557 1 57 2 110.0 196.0 225.00 144.00 44.00 96.5
## 558 1 59 2 98.0 176.0 185.00 113.00 42.00 76.0
## 559 1 54 2 91.0 151.0 254.00 162.00 64.00 83.5
## 560 1 52 2 118.0 209.0 220.00 138.00 69.00 84.3
## 561 1 45 2 116.0 161.0 174.00 111.00 51.00 84.0
## 562 1 42 2 117.0 166.0 265.00 201.00 41.00 88.0
## 563 1 49 2 100.0 116.0 333.00 214.00 39.00 107.0
## 564 1 41 2 95.0 197.0 275.00 255.00 43.00 87.0
## 565 1 57 2 87.0 222.0 156.00 73.00 10.00 88.0
## 566 1 32 2 324.0 382.0 210.00 157.00 45.00 83.0
## 567 1 61 2 292.0 513.0 303.00 236.00 47.00 91.0
## 568 1 58 2 117.0 261.0 274.00 222.00 38.00 91.0
## 569 1 48 1 100.0 133.0 267.00 225.00 45.00 95.0
## 570 1 55 1 341.0 459.0 211.00 141.00 54.00 112.8
## 571 1 52 2 85.0 100.0 223.00 164.00 48.00 81.0
## 572 1 58 1 91.0 96.0 250.00 183.00 49.00 105.0
## 573 1 59 1 102.0 146.0 315.00 287.00 39.00 101.0
## 574 1 60 2 89.0 113.0 233.00 177.00 37.00 91.0
## 575 1 58 1 101.0 169.0 336.00 229.00 49.00 87.0
## 576 1 64 2 92.0 156.0 202.00 113.00 46.00 84.0
## 577 1 63 2 85.0 202.0 294.00 252.00 48.00 93.0
## 578 1 30 2 97.0 179.0 202.00 159.00 45.00 107.5
## 579 1 60 2 88.0 173.0 380.00 278.00 51.00 98.0
## 580 1 60 2 107.0 124.0 280.00 207.00 57.00 87.6
## 581 1 61 1 100.0 185.0 240.00 149.00 70.00 87.0
## 582 1 58 2 103.0 200.0 321.00 237.00 67.00 89.0
## 583 1 61 1 79.0 106.0 267.00 191.00 51.00 90.0
## 584 1 52 2 93.0 110.0 241.00 163.00 55.00 96.0
## 585 1 56 2 109.0 115.0 273.00 222.00 39.00 95.0
## 586 1 51 2 109.0 222.0 321.00 234.00 41.00 92.0
## 587 1 45 1 89.0 150.0 167.00 82.00 41.00 80.0
## 588 1 39 2 106.0 209.0 278.00 219.00 50.00 83.0
## 589 1 49 1 320.0 411.0 241.00 180.00 47.00 87.0
## 590 1 42 2 665.0 677.0 298.00 252.00 33.00 85.0
## 591 1 58 2 159.0 272.0 434.00 243.00 69.00 104.0
## 592 1 63 2 162.0 210.0 203.00 142.00 33.00 83.0
## 593 1 47 2 91.0 117.0 288.00 223.00 49.00 85.0
## 594 1 40 2 160.0 252.0 320.00 260.00 36.00 86.0
## 595 1 52 2 90.0 157.0 219.00 148.00 62.00 88.0
## 596 1 43 2 101.0 149.0 197.00 108.00 52.00 86.0
## 597 1 37 1 114.0 107.0 207.00 152.00 44.00 88.0
## 598 1 51 1 115.0 147.0 297.00 180.00 77.00 93.5
## 599 1 55 2 101.0 120.0 217.00 108.00 48.00 89.1
## 600 1 56 1 108.0 176.0 192.00 119.00 43.00 97.0
## 601 1 43 2 113.0 177.0 311.00 216.00 54.00 76.0
## 602 1 55 2 114.0 198.0 270.00 141.00 36.00 83.0
## 603 1 53 2 150.0 245.0 300.00 178.00 40.00 104.0
## 604 1 51 1 85.0 148.0 124.00 62.00 37.00 71.0
## 605 1 47 2 100.0 169.0 216.00 134.00 46.00 80.0
## 606 1 51 1 91.0 118.0 192.00 106.00 48.00 92.0
## 607 1 57 2 208.0 383.0 326.00 187.00 46.00 95.5
## 608 1 44 2 109.0 275.0 287.00 159.00 35.00 90.0
## 609 1 60 2 131.0 255.0 244.00 147.00 33.00 108.6
## 610 1 49 2 96.0 130.0 267.00 180.00 41.00 92.0
## 611 1 49 1 422.0 612.0 276.00 217.00 52.00 72.0
## 612 1 57 2 91.0 164.0 194.00 113.00 46.00 74.0
## 613 1 43 1 86.0 128.0 245.00 139.00 60.00 86.0
## 614 1 49 2 143.0 315.0 148.00 87.00 30.00 75.0
## 615 1 54 1 93.0 128.0 368.00 217.00 47.00 101.5
## 616 1 59 2 107.0 186.0 148.00 99.00 33.00 88.5
## 617 1 45 1 105.0 172.0 246.00 141.00 59.00 81.5
## 618 1 46 2 149.0 222.0 247.00 169.00 54.00 93.0
## 619 1 43 1 95.0 160.0 186.00 116.00 48.00 110.0
## 620 1 57 2 84.0 170.0 245.00 169.00 45.00 95.0
## 621 1 64 1 117.0 217.0 230.00 173.00 45.00 97.0
## 622 1 57 1 98.0 134.0 306.00 192.00 40.00 86.0
## 623 1 49 2 99.0 167.0 235.00 129.00 36.00 94.0
## 624 1 44 1 102.0 128.0 227.00 114.00 47.00 98.0
## 625 1 53 2 96.0 152.0 235.00 165.00 37.00 77.0
## 626 1 46 2 83.0 92.0 290.00 172.00 62.00 82.0
## 627 1 52 1 137.0 210.0 193.00 100.00 46.00 97.5
## 628 1 44 2 115.0 186.0 272.00 189.00 52.00 90.0
## 629 1 60 2 95.0 221.0 230.00 168.00 44.00 93.0
## 630 1 58 2 127.0 261.0 224.00 148.00 60.00 95.0
## 631 1 53 1 120.0 117.0 219.00 145.00 34.00 96.0
## 632 1 46 2 113.0 124.0 197.00 98.00 45.00 95.0
## 633 1 50 1 96.0 189.0 283.00 200.00 41.00 102.0
## 634 1 65 1 138.0 163.0 241.00 112.00 61.00 77.0
## 635 1 35 2 113.0 123.0 187.00 102.00 37.00 92.5
## 636 1 38 1 105.0 160.0 182.00 116.00 38.00 84.0
## 637 1 47 2 96.0 142.0 261.00 158.00 72.00 67.0
## 638 1 63 2 227.0 532.0 177.00 131.00 31.00 102.6
## 639 1 42 1 99.0 147.0 220.00 133.00 65.00 85.5
## 640 1 54 1 96.0 147.0 359.00 248.00 58.00 87.0
## 641 1 37 2 102.0 155.0 208.00 123.00 43.00 68.0
## 642 1 41 2 91.0 114.0 214.00 116.00 55.00 86.0
## 643 1 41 2 85.0 115.0 200.00 125.00 58.00 92.0
## 644 1 47 1 116.0 114.0 241.00 190.00 36.00 91.0
## 645 1 55 1 93.0 115.0 255.00 163.00 45.00 107.0
## 646 1 49 2 102.0 131.0 237.00 160.00 40.00 104.0
## 647 1 59 2 121.0 154.0 259.00 166.00 44.00 101.0
## 648 1 54 1 91.0 142.0 239.00 161.00 65.00 89.0
## 649 1 48 1 99.0 159.0 265.00 195.00 41.00 91.0
## 650 1 45 2 130.0 158.0 216.00 85.00 56.00 71.0
## 651 1 58 2 102.0 230.0 266.00 185.00 57.00 88.0
## 652 1 60 2 112.0 134.0 210.00 124.00 53.00 89.0
## 653 1 55 2 105.0 140.0 381.00 223.00 42.00 97.0
## 654 1 42 1 91.0 105.0 261.00 136.00 42.00 92.3
## 655 1 57 2 91.0 133.0 232.00 167.00 37.00 85.0
## 656 1 35 2 84.0 130.0 196.00 126.00 44.00 90.3
## 657 1 48 2 145.0 157.0 245.00 129.00 44.00 97.0
## 658 1 54 2 115.0 201.0 255.00 131.00 49.00 74.0
## 659 1 56 1 139.0 182.0 267.00 152.00 51.00 98.0
## 660 1 46 2 114.0 113.0 214.00 138.00 50.00 84.0
## 661 1 52 2 121.0 182.0 255.00 162.00 62.00 84.0
## 662 1 59 1 138.0 179.0 254.00 162.00 41.00 95.0
## 663 1 40 1 87.0 142.0 242.00 171.00 57.00 94.0
## 664 1 54 2 319.0 391.0 319.00 241.00 41.00 107.0
## 665 1 50 2 128.0 291.0 312.00 243.00 60.00 90.5
## 666 1 52 1 122.0 197.0 216.00 141.00 46.00 86.7
## 667 1 42 2 90.0 148.0 252.00 181.00 46.00 94.0
## 668 1 47 1 119.0 207.0 286.00 237.00 52.00 74.0
## 669 1 46 2 103.0 124.0 162.00 125.00 32.00 75.0
## 670 1 53 2 100.0 139.0 296.00 117.00 42.00 95.0
## 671 1 56 2 95.0 114.0 279.00 183.00 47.00 82.5
## 672 1 57 2 112.0 142.0 317.00 191.00 39.00 93.5
## 673 1 49 2 261.0 343.0 282.00 180.00 58.00 107.0
## 674 1 55 2 277.0 354.0 270.00 170.00 40.00 92.0
## 675 1 37 2 94.0 175.0 182.00 112.00 44.00 80.0
## 676 1 63 1 216.0 290.0 301.00 252.00 53.00 118.0
## 677 1 57 2 120.2 158.2 273.00 183.00 35.34 82.5
## 678 1 38 2 93.0 189.0 154.00 73.00 42.00 77.0
## 679 1 39 1 107.0 212.2 217.00 150.00 45.70 100.0
## 680 1 44 2 98.0 130.0 244.90 172.60 42.70 85.3
## 681 1 50 2 96.0 131.6 179.00 103.00 49.00 100.0
## 682 1 41 2 115.0 186.0 195.00 120.00 57.80 97.5
## 683 1 39 2 94.0 161.0 179.00 111.00 44.00 98.5
## 684 1 33 1 84.0 108.0 194.00 142.00 42.10 77.0
## 685 1 39 2 95.0 123.0 234.00 164.00 54.40 94.4
## 686 1 48 1 354.0 406.0 253.00 169.00 44.00 95.8
## 687 1 60 2 82.0 167.0 216.00 134.00 61.00 70.1
## 688 1 47 2 92.0 130.0 233.00 156.00 58.40 90.0
## 689 1 47 2 98.0 122.0 232.00 122.10 48.00 91.0
## 690 1 28 2 109.0 108.0 168.00 100.00 24.00 100.0
## 691 1 26 2 84.0 94.7 178.00 110.00 51.00 73.0
## 692 1 26 2 90.0 108.2 155.00 107.00 36.00 84.2
## 693 1 45 2 266.0 324.0 259.00 177.00 52.00 85.5
## Berat_Badan Target_Hipertensi Nyeri_Dada Riwayat_Keluarga Sistolik
## 1 42.0 0 0 0 82
## 2 51.9 0 0 0 94
## 3 82.3 0 0 0 116
## 4 60.2 0 0 0 114
## 5 48.5 0 0 0 86
## 6 67.0 0 0 0 92
## 7 58.9 0 0 0 106
## 8 50.2 0 0 0 106
## 9 46.2 0 0 0 90
## 10 65.0 0 0 0 102
## 11 58.7 0 0 0 107
## 12 47.4 0 0 0 90
## 13 45.1 0 0 0 94
## 14 63.2 0 0 0 102
## 15 70.9 0 0 0 120
## 16 77.5 0 0 0 127
## 17 60.8 0 0 0 99
## 18 68.1 0 0 0 122
## 19 52.8 0 0 0 84
## 20 56.6 0 0 0 102
## 21 50.9 0 0 0 97
## 22 48.3 0 0 0 114
## 23 54.8 1 1 0 145
## 24 77.0 0 0 0 110
## 25 83.0 0 0 0 91
## 26 52.2 0 0 0 104
## 27 62.3 0 0 0 108
## 28 53.6 0 0 0 108
## 29 57.2 1 0 0 154
## 30 55.5 0 0 0 78
## 31 64.5 0 0 0 89
## 32 39.8 0 0 0 100
## 33 50.9 0 0 0 105
## 34 54.3 0 0 0 98
## 35 57.6 1 0 0 131
## 36 57.8 0 0 0 116
## 37 63.0 0 0 0 90
## 38 62.1 0 0 0 99
## 39 60.6 0 0 0 108
## 40 67.7 0 0 0 112
## 41 75.0 0 0 0 99
## 42 48.1 0 0 0 90
## 43 57.0 0 0 0 98
## 44 51.8 0 0 0 90
## 45 61.0 0 0 0 102
## 46 46.4 0 0 0 137
## 47 60.7 0 0 0 122
## 48 60.7 0 0 0 115
## 49 56.4 0 0 0 104
## 50 58.7 0 0 0 111
## 51 44.0 0 0 0 116
## 52 76.2 0 0 0 104
## 53 69.0 0 0 0 110
## 54 62.3 0 0 0 102
## 55 54.3 0 0 0 105
## 56 45.3 0 0 0 83
## 57 51.8 0 0 0 109
## 58 36.5 0 0 0 117
## 59 48.5 0 0 0 111
## 60 47.9 0 0 0 90
## 61 92.1 0 0 0 108
## 62 42.9 0 0 0 107
## 63 54.2 0 0 0 125
## 64 42.7 0 0 0 98
## 65 49.6 0 0 0 92
## 66 40.2 0 0 0 89
## 67 46.0 0 0 0 112
## 68 79.1 0 0 0 107
## 69 64.7 0 0 0 92
## 70 54.0 0 0 0 105
## 71 65.3 0 0 0 116
## 72 54.9 0 0 0 103
## 73 56.6 0 0 0 92
## 74 61.5 1 0 0 140
## 75 59.4 0 0 0 99
## 76 46.3 0 0 0 96
## 77 60.7 0 0 0 117
## 78 62.7 0 0 0 113
## 79 63.4 0 0 0 92
## 80 78.6 0 0 0 99
## 81 68.4 0 0 0 123
## 82 55.2 0 0 0 91
## 83 63.8 0 0 0 118
## 84 48.0 0 0 0 83
## 85 50.6 0 0 0 109
## 86 53.0 1 0 0 138
## 87 54.0 0 0 0 122
## 88 50.4 0 0 0 104
## 89 56.0 0 0 0 90
## 90 55.0 0 0 0 122
## 91 57.7 0 0 0 102
## 92 54.1 0 0 0 102
## 93 52.9 0 0 0 113
## 94 52.5 0 0 0 94
## 95 61.0 0 0 0 104
## 96 48.4 0 0 0 90
## 97 52.3 0 0 0 74
## 98 37.2 0 0 0 84
## 99 44.3 0 0 0 107
## 100 44.3 0 0 0 115
## 101 42.2 0 0 0 93
## 102 57.6 0 0 0 98
## 103 51.3 0 0 0 97
## 104 73.9 0 0 0 100
## 105 69.3 0 0 0 97
## 106 67.8 0 0 0 107
## 107 47.4 0 0 0 92
## 108 63.8 0 0 0 86
## 109 50.8 0 0 0 123
## 110 39.4 0 0 0 79
## 111 68.3 0 0 0 103
## 112 72.2 0 0 0 89
## 113 53.6 1 0 0 138
## 114 72.4 0 0 0 110
## 115 41.7 0 1 0 126
## 116 47.9 0 0 0 106
## 117 48.1 1 0 0 144
## 118 93.5 0 0 0 114
## 119 51.5 0 0 0 78
## 120 76.5 0 0 0 104
## 121 45.9 0 0 0 88
## 122 58.8 0 0 0 93
## 123 58.3 0 0 0 106
## 124 59.7 0 0 0 118
## 125 33.9 0 0 0 94
## 126 52.6 0 0 0 122
## 127 55.9 0 0 0 123
## 128 62.2 0 0 0 102
## 129 65.7 0 0 0 97
## 130 62.4 0 0 0 114
## 131 58.3 0 0 0 114
## 132 73.1 0 0 0 113
## 133 52.4 0 0 0 98
## 134 53.8 0 0 0 83
## 135 46.9 0 0 0 90
## 136 45.6 0 0 0 116
## 137 60.7 0 0 0 100
## 138 55.0 0 0 0 100
## 139 53.0 0 0 0 110
## 140 50.0 0 0 0 101
## 141 59.7 0 0 0 127
## 142 47.3 0 0 0 109
## 143 65.6 0 0 0 99
## 144 74.6 1 0 0 146
## 145 55.9 0 0 0 100
## 146 59.8 1 0 0 132
## 147 56.6 0 0 0 118
## 148 53.2 0 0 0 112
## 149 69.3 1 0 0 138
## 150 49.7 0 0 0 103
## 151 94.9 0 0 0 117
## 152 44.0 0 0 0 98
## 153 58.0 0 0 0 96
## 154 60.0 0 0 0 97
## 155 42.0 0 0 0 85
## 156 42.7 0 1 0 106
## 157 56.5 0 0 0 107
## 158 58.6 0 0 0 117
## 159 80.0 1 0 0 140
## 160 64.0 0 0 0 110
## 161 53.5 0 0 0 98
## 162 56.0 0 0 0 98
## 163 45.9 0 0 0 87
## 164 61.3 0 0 0 101
## 165 41.8 0 0 0 99
## 166 51.4 0 0 0 126
## 167 52.7 0 0 0 115
## 168 60.4 0 0 0 98
## 169 52.0 0 0 0 102
## 170 69.0 0 0 0 110
## 171 58.9 1 0 0 147
## 172 78.1 0 0 0 115
## 173 53.3 0 0 0 104
## 174 57.9 0 0 0 94
## 175 45.4 0 0 0 92
## 176 85.6 0 0 0 97
## 177 41.3 0 0 0 72
## 178 54.4 0 0 0 105
## 179 53.4 0 0 0 121
## 180 73.4 0 0 0 100
## 181 47.4 0 0 0 102
## 182 60.1 0 0 0 105
## 183 58.1 0 0 0 107
## 184 44.4 0 0 0 89
## 185 65.3 0 0 0 109
## 186 47.1 0 0 0 88
## 187 39.4 0 0 0 89
## 188 49.5 0 0 0 90
## 189 46.0 0 0 0 127
## 190 75.4 0 0 0 93
## 191 63.3 0 0 0 100
## 192 49.3 1 0 0 208
## 193 34.1 0 0 0 100
## 194 57.5 0 0 0 95
## 195 50.8 0 0 0 105
## 196 54.4 0 0 0 93
## 197 72.0 0 0 0 105
## 198 56.3 0 0 0 99
## 199 52.6 0 0 0 118
## 200 57.7 0 0 0 115
## 201 74.6 0 0 0 126
## 202 81.1 1 0 0 155
## 203 52.6 0 0 0 114
## 204 47.0 0 0 0 100
## 205 40.9 0 0 0 90
## 206 62.4 0 0 0 98
## 207 47.3 0 0 0 93
## 208 51.4 0 0 0 102
## 209 39.1 0 0 0 117
## 210 73.9 0 0 0 121
## 211 52.8 0 0 0 95
## 212 51.8 0 0 0 114
## 213 60.3 0 0 0 104
## 214 55.9 0 0 0 92
## 215 52.6 0 0 0 104
## 216 76.3 1 0 0 168
## 217 49.4 0 0 0 130
## 218 50.2 0 0 0 93
## 219 42.3 0 1 0 101
## 220 65.2 0 0 0 107
## 221 45.5 0 0 0 88
## 222 39.1 0 0 0 85
## 223 43.2 0 0 0 114
## 224 54.0 0 0 0 111
## 225 45.1 0 0 0 122
## 226 75.5 0 0 0 112
## 227 63.0 0 0 0 95
## 228 47.2 0 0 0 89
## 229 68.0 0 0 0 126
## 230 64.6 1 0 0 130
## 231 76.7 0 0 0 114
## 232 67.5 0 0 0 99
## 233 65.1 0 0 0 108
## 234 57.6 0 0 0 103
## 235 53.0 0 1 0 111
## 236 41.9 0 0 0 77
## 237 54.3 0 0 0 138
## 238 78.6 0 0 0 116
## 239 57.6 0 0 0 80
## 240 71.4 0 0 0 99
## 241 72.9 1 0 0 147
## 242 71.0 0 0 0 100
## 243 49.8 0 0 0 117
## 244 47.5 0 0 0 95
## 245 72.0 0 0 0 100
## 246 50.3 0 0 0 98
## 247 64.8 0 0 0 100
## 248 46.0 0 0 0 97
## 249 42.0 0 0 0 93
## 250 70.2 0 0 0 94
## 251 44.2 0 0 0 84
## 252 46.3 0 0 0 93
## 253 45.6 0 0 0 108
## 254 48.3 0 0 0 100
## 255 53.5 0 0 0 114
## 256 38.5 0 0 0 85
## 257 62.0 0 0 0 123
## 258 78.2 0 0 0 132
## 259 73.2 0 0 0 114
## 260 50.8 0 0 0 103
## 261 55.1 0 0 0 92
## 262 53.2 0 0 0 99
## 263 54.7 0 0 0 97
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## 562 110 0
## 563 169 1
## 564 100 0
## 565 72 0
## 566 97 0
## 567 134 0
## 568 113 0
## 569 89 0
## 570 104 0
## 571 76 0
## 572 94 0
## 573 90 0
## 574 86 1
## 575 95 1
## 576 93 0
## 577 84 0
## 578 101 1
## 579 86 1
## 580 97 0
## 581 88 1
## 582 96 0
## 583 90 0
## 584 92 1
## 585 110 0
## 586 91 1
## 587 86 0
## 588 104 0
## 589 104 0
## 590 100 0
## 591 110 1
## 592 79 1
## 593 102 1
## 594 98 1
## 595 124 0
## 596 93 0
## 597 107 0
## 598 95 0
## 599 92 0
## 600 137 0
## 601 110 0
## 602 105 0
## 603 132 1
## 604 80 1
## 605 110 0
## 606 94 0
## 607 97 0
## 608 91 0
## 609 93 0
## 610 150 0
## 611 98 1
## 612 99 0
## 613 86 1
## 614 108 0
## 615 113 0
## 616 84 1
## 617 85 0
## 618 91 0
## 619 110 0
## 620 76 0
## 621 102 0
## 622 98 1
## 623 91 1
## 624 89 1
## 625 90 1
## 626 92 0
## 627 86 0
## 628 109 0
## 629 102 0
## 630 101 0
## 631 100 1
## 632 107 0
## 633 93 1
## 634 94 1
## 635 86 0
## 636 97 1
## 637 85 0
## 638 110 0
## 639 99 0
## 640 106 0
## 641 105 0
## 642 111 0
## 643 114 0
## 644 92 0
## 645 120 1
## 646 110 0
## 647 102 0
## 648 85 0
## 649 105 1
## 650 102 0
## 651 98 0
## 652 82 0
## 653 120 1
## 654 107 1
## 655 145 1
## 656 102 1
## 657 142 0
## 658 103 0
## 659 102 0
## 660 113 0
## 661 107 0
## 662 102 0
## 663 90 1
## 664 111 0
## 665 98 0
## 666 92 0
## 667 91 0
## 668 82 0
## 669 82 1
## 670 96 1
## 671 82 1
## 672 107 1
## 673 100 1
## 674 105 0
## 675 141 0
## 676 114 0
## 677 114 1
## 678 126 0
## 679 122 1
## 680 76 0
## 681 113 0
## 682 83 1
## 683 112 0
## 684 101 0
## 685 114 0
## 686 89 0
## 687 132 1
## 688 108 0
## 689 75 0
## 690 98 1
## 691 81 0
## 692 108 0
## 693 100 0
# Menghitung proporsi kelas
prop_table <- prop.table(table(undersampled_reglog$Target_PJK))
print(prop_table)
##
## 0 1
## 0.5223665 0.4776335
table(undersampled_reglog$Target_PJK)
##
## 0 1
## 362 331
Regresi Logistik Biner
Signifikansi Peubah
Data Train
reglog_train <- glm(Target_PJK ~ ., data = train_dt, family = binomial(link = "logit"))
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summ <- summary(reglog_train)
summ
##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = train_dt)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.6908 -0.0816 -0.0360 -0.0153 3.5565
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -22.708656 2.321765 -9.781 < 2e-16 ***
## Age 0.019873 0.015249 1.303 0.19249
## Sex2 -0.186401 0.300663 -0.620 0.53528
## GD_Puasa 0.006173 0.006168 1.001 0.31686
## GD_PP 0.001441 0.003515 0.410 0.68180
## Kolestrol 0.003188 0.005677 0.562 0.57444
## LDL 0.002774 0.005905 0.470 0.63856
## HDL -0.042548 0.015057 -2.826 0.00472 **
## Lipoprotein 0.120240 0.020606 5.835 5.37e-09 ***
## Berat_Badan -0.098922 0.016420 -6.025 1.70e-09 ***
## Target_Hipertensi1 0.347922 0.457283 0.761 0.44675
## Nyeri_Dada 3.002927 0.366520 8.193 2.55e-16 ***
## Riwayat_Keluarga1 20.101958 692.438431 0.029 0.97684
## Sistolik 0.029913 0.011087 2.698 0.00697 **
## Diastolik 0.127375 0.019331 6.589 4.43e-11 ***
## Merokok 0.264505 0.337047 0.785 0.43259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2235.11 on 3729 degrees of freedom
## Residual deviance: 377.47 on 3714 degrees of freedom
## AIC: 409.47
##
## Number of Fisher Scoring iterations: 18
# Mengimpor paket 'car'
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:gtools':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
# Menghitung VIF
vif_model <- vif(reglog_train)
vif_model
## Age Sex GD_Puasa GD_PP
## 1.151742 1.038072 3.961753 4.095025
## Kolestrol LDL HDL Lipoprotein
## 2.823979 2.675496 1.218576 2.307610
## Berat_Badan Target_Hipertensi Nyeri_Dada Riwayat_Keluarga
## 2.411212 2.534642 1.140072 1.000000
## Sistolik Diastolik Merokok
## 2.762148 2.314593 1.160728
Uji Parsial
Anova (reglog_train, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Age 1 1.6984 0.192493
## Sex 1 0.3844 0.535279
## GD_Puasa 1 1.0019 0.316857
## GD_PP 1 0.1681 0.681802
## Kolestrol 1 0.3153 0.574438
## LDL 1 0.2206 0.638559
## HDL 1 7.9846 0.004718 **
## Lipoprotein 1 34.0514 5.368e-09 ***
## Berat_Badan 1 36.2949 1.696e-09 ***
## Target_Hipertensi 1 0.5789 0.446749
## Nyeri_Dada 1 67.1265 2.546e-16 ***
## Riwayat_Keluarga 1 0.0008 0.976840
## Sistolik 1 7.2798 0.006974 **
## Diastolik 1 43.4148 4.428e-11 ***
## Merokok 1 0.6159 0.432587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GOF
library(ResourceSelection)
## Warning: package 'ResourceSelection' was built under R version 4.2.3
## ResourceSelection 0.3-5 2019-07-22
hoslem.test(reglog_train$y, fitted(reglog_train))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: reglog_train$y, fitted(reglog_train)
## X-squared = 2.627, df = 8, p-value = 0.9555
pred1 <- predict(reglog_train, test_dt, type = "response")
predicted <- round(pred1)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1435 20
## 1 21 121
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1435 20
## 1 21 121
##
## Accuracy : 0.9743
## 95% CI : (0.9653, 0.9815)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.841
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9856
## Specificity : 0.8582
## Pos Pred Value : 0.9863
## Neg Pred Value : 0.8521
## Prevalence : 0.9117
## Detection Rate : 0.8986
## Detection Prevalence : 0.9111
## Balanced Accuracy : 0.9219
##
## 'Positive' Class : 0
##
Smote
library(car)
reglog_smote <- glm(Target_PJK ~ ., data = smote_reglog, family = binomial(link = "logit"))
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summ <- summary(reglog_smote)
summ
##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = smote_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.8727 -0.0684 -0.0009 0.0092 2.9649
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -24.358355 2.197624 -11.084 < 2e-16 ***
## Age 0.026741 0.015424 1.734 0.083 .
## Sex2 -0.229395 0.267353 -0.858 0.391
## GD_Puasa 0.008478 0.007060 1.201 0.230
## GD_PP 0.003425 0.003440 0.995 0.320
## Kolestrol 0.001436 0.007326 0.196 0.845
## LDL 0.008813 0.007414 1.189 0.235
## HDL -0.051625 0.013162 -3.922 8.77e-05 ***
## Lipoprotein 0.138502 0.021033 6.585 4.55e-11 ***
## Berat_Badan -0.139882 0.018056 -7.747 9.41e-15 ***
## Target_Hipertensi1 0.262414 0.349572 0.751 0.453
## Nyeri_Dada 3.416428 0.373549 9.146 < 2e-16 ***
## Riwayat_Keluarga1 18.628288 514.833175 0.036 0.971
## Sistolik 0.051658 0.012295 4.202 2.65e-05 ***
## Diastolik 0.138030 0.018957 7.281 3.31e-13 ***
## Merokok 0.368076 0.295485 1.246 0.213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4158.88 on 2999 degrees of freedom
## Residual deviance: 482.71 on 2984 degrees of freedom
## AIC: 514.71
##
## Number of Fisher Scoring iterations: 19
GOF
library(ResourceSelection)
hoslem.test(reglog_smote$y, fitted(reglog_smote))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: reglog_smote$y, fitted(reglog_smote)
## X-squared = 6.7371, df = 8, p-value = 0.5652
Uji Simultan
library(pscl)
## Warning: package 'pscl' was built under R version 4.2.3
## Classes and Methods for R developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University
## Simon Jackman
## hurdle and zeroinfl functions by Achim Zeileis
pR2(reglog_smote)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML
## -241.3547001 -2079.4415417 3676.1736831 0.8839329 0.7063572
## r2CU
## 0.9418096
qchisq(0.95, 18)
## [1] 28.8693
Uji Parsial
Anova (reglog_smote, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Age 1 3.0057 0.08297 .
## Sex 1 0.7362 0.39088
## GD_Puasa 1 1.4420 0.22981
## GD_PP 1 0.9909 0.31951
## Kolestrol 1 0.0384 0.84460
## LDL 1 1.4128 0.23459
## HDL 1 15.3849 8.769e-05 ***
## Lipoprotein 1 43.3616 4.550e-11 ***
## Berat_Badan 1 60.0166 9.406e-15 ***
## Target_Hipertensi 1 0.5635 0.45285
## Nyeri_Dada 1 83.6469 < 2.2e-16 ***
## Riwayat_Keluarga 1 0.0013 0.97114
## Sistolik 1 17.6537 2.650e-05 ***
## Diastolik 1 53.0162 3.308e-13 ***
## Merokok 1 1.5517 0.21289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
options(digits = 3)
beta = coef(reglog_smote)
OR = exp(beta)
cbind(beta, OR)
## beta OR
## (Intercept) -24.35835 2.64e-11
## Age 0.02674 1.03e+00
## Sex2 -0.22939 7.95e-01
## GD_Puasa 0.00848 1.01e+00
## GD_PP 0.00342 1.00e+00
## Kolestrol 0.00144 1.00e+00
## LDL 0.00881 1.01e+00
## HDL -0.05163 9.50e-01
## Lipoprotein 0.13850 1.15e+00
## Berat_Badan -0.13988 8.69e-01
## Target_Hipertensi1 0.26241 1.30e+00
## Nyeri_Dada 3.41643 3.05e+01
## Riwayat_Keluarga1 18.62829 1.23e+08
## Sistolik 0.05166 1.05e+00
## Diastolik 0.13803 1.15e+00
## Merokok 0.36808 1.44e+00
pred2 <- predict(reglog_smote, test_dt, type = "response")
predicted <- round(pred2)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1403 8
## 1 53 133
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1403 8
## 1 53 133
##
## Accuracy : 0.962
## 95% CI : (0.951, 0.971)
## No Information Rate : 0.912
## P-Value [Acc > NIR] : 2.87e-15
##
## Kappa : 0.793
##
## Mcnemar's Test P-Value : 1.76e-08
##
## Sensitivity : 0.964
## Specificity : 0.943
## Pos Pred Value : 0.994
## Neg Pred Value : 0.715
## Prevalence : 0.912
## Detection Rate : 0.879
## Detection Prevalence : 0.884
## Balanced Accuracy : 0.953
##
## 'Positive' Class : 0
##
Oversampling
library(car)
reglog_over <- glm(Target_PJK ~ ., data = oversampling_reglog, family = binomial(link = "logit"))
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summ <- summary(reglog_over)
summ
##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = oversampling_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.675 -0.171 -0.022 0.028 3.573
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.99342 1.13104 -15.91 < 2e-16 ***
## Age 0.02135 0.00770 2.77 0.00555 **
## Sex2 0.07862 0.19060 0.41 0.68000
## GD_Puasa 0.00309 0.00201 1.54 0.12289
## GD_PP 0.00207 0.00125 1.66 0.09756 .
## Kolestrol 0.00247 0.00233 1.06 0.28850
## LDL 0.00958 0.00252 3.80 0.00015 ***
## HDL -0.00578 0.00301 -1.92 0.05489 .
## Lipoprotein 0.04706 0.00762 6.18 6.5e-10 ***
## Berat_Badan -0.03305 0.00682 -4.85 1.3e-06 ***
## Target_Hipertensi1 1.65530 0.23877 6.93 4.1e-12 ***
## Nyeri_Dada 2.42782 0.22328 10.87 < 2e-16 ***
## Riwayat_Keluarga1 20.20470 466.92758 0.04 0.96549
## Sistolik 0.03622 0.00529 6.85 7.3e-12 ***
## Diastolik 0.07837 0.00874 8.96 < 2e-16 ***
## Merokok 0.31478 0.17546 1.79 0.07280 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5170.75 on 3729 degrees of freedom
## Residual deviance: 926.08 on 3714 degrees of freedom
## AIC: 958.1
##
## Number of Fisher Scoring iterations: 19
GOF
library(ResourceSelection)
hoslem.test(reglog_over$y, fitted(reglog_over))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: reglog_over$y, fitted(reglog_over)
## X-squared = 46, df = 8, p-value = 3e-07
Uji Simultan
library(pscl)
pR2(reglog_over)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -463.042 -2585.374 4244.665 0.821 0.680 0.906
qchisq(0.95, 18)
## [1] 28.9
Uji Parsial
Anova (reglog_over, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Age 1 7.69 0.00555 **
## Sex 1 0.17 0.68000
## GD_Puasa 1 2.38 0.12289
## GD_PP 1 2.74 0.09756 .
## Kolestrol 1 1.13 0.28850
## LDL 1 14.42 0.00015 ***
## HDL 1 3.69 0.05489 .
## Lipoprotein 1 38.17 6.5e-10 ***
## Berat_Badan 1 23.49 1.3e-06 ***
## Target_Hipertensi 1 48.06 4.1e-12 ***
## Nyeri_Dada 1 118.23 < 2e-16 ***
## Riwayat_Keluarga 1 0.00 0.96549
## Sistolik 1 46.93 7.3e-12 ***
## Diastolik 1 80.31 < 2e-16 ***
## Merokok 1 3.22 0.07280 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
options(digits = 3)
beta = coef(reglog_over)
OR = exp(beta)
cbind(beta, OR)
## beta OR
## (Intercept) -17.99342 1.53e-08
## Age 0.02135 1.02e+00
## Sex2 0.07862 1.08e+00
## GD_Puasa 0.00309 1.00e+00
## GD_PP 0.00207 1.00e+00
## Kolestrol 0.00247 1.00e+00
## LDL 0.00958 1.01e+00
## HDL -0.00578 9.94e-01
## Lipoprotein 0.04706 1.05e+00
## Berat_Badan -0.03305 9.67e-01
## Target_Hipertensi1 1.65530 5.23e+00
## Nyeri_Dada 2.42782 1.13e+01
## Riwayat_Keluarga1 20.20470 5.95e+08
## Sistolik 0.03622 1.04e+00
## Diastolik 0.07837 1.08e+00
## Merokok 0.31478 1.37e+00
pred3 <- predict(reglog_over, test_dt, type = "response")
predicted <- round(pred3)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1394 6
## 1 62 135
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1394 6
## 1 62 135
##
## Accuracy : 0.957
## 95% CI : (0.946, 0.967)
## No Information Rate : 0.912
## P-Value [Acc > NIR] : 1.02e-12
##
## Kappa : 0.776
##
## Mcnemar's Test P-Value : 2.56e-11
##
## Sensitivity : 0.957
## Specificity : 0.957
## Pos Pred Value : 0.996
## Neg Pred Value : 0.685
## Prevalence : 0.912
## Detection Rate : 0.873
## Detection Prevalence : 0.877
## Balanced Accuracy : 0.957
##
## 'Positive' Class : 0
##
Undersampling
library(car)
reglog_under <- glm(Target_PJK ~ ., data = undersampled_reglog, family = binomial(link = "logit"))
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summ <- summary(reglog_under)
summ
##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = undersampled_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.598 -0.083 -0.007 0.009 3.078
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.80e+01 4.56e+00 -6.15 8.0e-10 ***
## Age 1.87e-02 2.63e-02 0.71 0.47610
## Sex2 1.93e-01 5.14e-01 0.37 0.70802
## GD_Puasa -4.82e-03 1.05e-02 -0.46 0.64524
## GD_PP 8.73e-03 5.75e-03 1.52 0.12869
## Kolestrol -3.52e-03 1.05e-02 -0.34 0.73615
## LDL 1.19e-02 1.06e-02 1.13 0.25929
## HDL -1.92e-02 2.29e-02 -0.84 0.40075
## Lipoprotein 1.33e-01 3.86e-02 3.44 0.00058 ***
## Berat_Badan -8.33e-02 3.46e-02 -2.41 0.01611 *
## Target_Hipertensi1 -4.50e-01 7.34e-01 -0.61 0.54007
## Nyeri_Dada 3.23e+00 6.89e-01 4.69 2.8e-06 ***
## Riwayat_Keluarga1 1.94e+01 1.08e+03 0.02 0.98570
## Sistolik 4.54e-02 2.12e-02 2.14 0.03232 *
## Diastolik 1.57e-01 3.23e-02 4.86 1.2e-06 ***
## Merokok 1.76e-01 5.40e-01 0.33 0.74452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 959.31 on 692 degrees of freedom
## Residual deviance: 135.54 on 677 degrees of freedom
## AIC: 167.5
##
## Number of Fisher Scoring iterations: 19
GOF
library(ResourceSelection)
hoslem.test(reglog_under$y, fitted(reglog_under))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: reglog_under$y, fitted(reglog_under)
## X-squared = 38, df = 8, p-value = 8e-06
Uji Simultan
library(pscl)
pR2(reglog_under)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -67.769 -479.657 823.777 0.859 0.695 0.928
qchisq(0.95, 18)
## [1] 28.9
Uji Parsial
Anova (reglog_under, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Age 1 0.51 0.47610
## Sex 1 0.14 0.70802
## GD_Puasa 1 0.21 0.64524
## GD_PP 1 2.31 0.12869
## Kolestrol 1 0.11 0.73615
## LDL 1 1.27 0.25929
## HDL 1 0.71 0.40075
## Lipoprotein 1 11.85 0.00058 ***
## Berat_Badan 1 5.79 0.01611 *
## Target_Hipertensi 1 0.38 0.54007
## Nyeri_Dada 1 21.97 2.8e-06 ***
## Riwayat_Keluarga 1 0.00 0.98570
## Sistolik 1 4.58 0.03232 *
## Diastolik 1 23.59 1.2e-06 ***
## Merokok 1 0.11 0.74452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
options(digits = 3)
beta = coef(reglog_under)
OR = exp(beta)
cbind(beta, OR)
## beta OR
## (Intercept) -28.01413 6.82e-13
## Age 0.01875 1.02e+00
## Sex2 0.19253 1.21e+00
## GD_Puasa -0.00482 9.95e-01
## GD_PP 0.00873 1.01e+00
## Kolestrol -0.00352 9.96e-01
## LDL 0.01193 1.01e+00
## HDL -0.01924 9.81e-01
## Lipoprotein 0.13273 1.14e+00
## Berat_Badan -0.08331 9.20e-01
## Target_Hipertensi1 -0.44958 6.38e-01
## Nyeri_Dada 3.22906 2.53e+01
## Riwayat_Keluarga1 19.37001 2.58e+08
## Sistolik 0.04538 1.05e+00
## Diastolik 0.15701 1.17e+00
## Merokok 0.17587 1.19e+00
pred4 <- predict(reglog_under, test_dt, type = "response")
predicted <- round(pred4)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1406 8
## 1 50 133
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1406 8
## 1 50 133
##
## Accuracy : 0.964
## 95% CI : (0.953, 0.972)
## No Information Rate : 0.912
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.801
##
## Mcnemar's Test P-Value : 7.3e-08
##
## Sensitivity : 0.966
## Specificity : 0.943
## Pos Pred Value : 0.994
## Neg Pred Value : 0.727
## Prevalence : 0.912
## Detection Rate : 0.880
## Detection Prevalence : 0.885
## Balanced Accuracy : 0.954
##
## 'Positive' Class : 0
##
# stepwise: automatic model selection method
options(warn=-1)
log_model_all <- glm(Target_PJK ~ ., family="binomial", data= train_dt)
log_model_nothing <- glm(Target_PJK ~ 1, family="binomial", data= train_dt)
log_model1 <- step(log_model_nothing,
list(lower=formula(log_model_nothing),
upper=formula(log_model_all)),
direction="both", trace = F, test= "F")
formula(log_model1)
## Target_PJK ~ Diastolik + Riwayat_Keluarga + Nyeri_Dada + Sistolik +
## Lipoprotein + Berat_Badan + HDL + GD_Puasa + Kolestrol +
## Age
log_model1 <- glm(Target_PJK ~ Diastolik + Riwayat_Keluarga + Nyeri_Dada + Sistolik +
Lipoprotein + Berat_Badan + HDL + GD_Puasa + Kolestrol +
Age,
family = "binomial", data = train_dt)
summary(log_model1)
##
## Call:
## glm(formula = Target_PJK ~ Diastolik + Riwayat_Keluarga + Nyeri_Dada +
## Sistolik + Lipoprotein + Berat_Badan + HDL + GD_Puasa + Kolestrol +
## Age, family = "binomial", data = train_dt)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.779 -0.082 -0.035 -0.014 3.539
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -23.66013 2.06329 -11.47 < 2e-16 ***
## Diastolik 0.13372 0.01845 7.25 4.2e-13 ***
## Riwayat_Keluarga1 20.10186 669.99104 0.03 0.97606
## Nyeri_Dada 3.00152 0.35614 8.43 < 2e-16 ***
## Sistolik 0.03373 0.00999 3.38 0.00074 ***
## Lipoprotein 0.11963 0.02035 5.88 4.2e-09 ***
## Berat_Badan -0.09727 0.01636 -5.94 2.8e-09 ***
## HDL -0.04693 0.01446 -3.25 0.00117 **
## GD_Puasa 0.00838 0.00317 2.64 0.00819 **
## Kolestrol 0.00546 0.00357 1.53 0.12549
## Age 0.02141 0.01505 1.42 0.15492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2235.11 on 3729 degrees of freedom
## Residual deviance: 379.38 on 3719 degrees of freedom
## AIC: 401.4
##
## Number of Fisher Scoring iterations: 18
log_model1 <- update(log_model1, .~. -Riwayat_Keluarga)
summary(log_model1)
##
## Call:
## glm(formula = Target_PJK ~ Diastolik + Nyeri_Dada + Sistolik +
## Lipoprotein + Berat_Badan + HDL + GD_Puasa + Kolestrol +
## Age, family = "binomial", data = train_dt)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.992 -0.098 -0.041 -0.016 3.466
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -22.60617 1.74020 -12.99 < 2e-16 ***
## Diastolik 0.13770 0.01622 8.49 < 2e-16 ***
## Nyeri_Dada 2.93876 0.31402 9.36 < 2e-16 ***
## Sistolik 0.03207 0.00884 3.63 0.00029 ***
## Lipoprotein 0.11754 0.01830 6.42 1.3e-10 ***
## Berat_Badan -0.10288 0.01494 -6.89 5.7e-12 ***
## HDL -0.06236 0.01285 -4.85 1.2e-06 ***
## GD_Puasa 0.00796 0.00288 2.77 0.00568 **
## Kolestrol 0.00888 0.00307 2.90 0.00379 **
## Age 0.01962 0.01368 1.43 0.15150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2235.11 on 3729 degrees of freedom
## Residual deviance: 500.29 on 3720 degrees of freedom
## AIC: 520.3
##
## Number of Fisher Scoring iterations: 8
GOF
library(ResourceSelection)
hoslem.test(log_model1$y, fitted(log_model1))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: log_model1$y, fitted(log_model1)
## X-squared = 3, df = 8, p-value = 0.9
Uji Simultan
library(pscl)
pR2(log_model1)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -250.144 -1117.556 1734.824 0.776 0.372 0.825
qchisq(0.95, 18)
## [1] 28.9
Uji Parsial
Anova (log_model1, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Diastolik 1 72.03 < 2e-16 ***
## Nyeri_Dada 1 87.58 < 2e-16 ***
## Sistolik 1 13.15 0.00029 ***
## Lipoprotein 1 41.25 1.3e-10 ***
## Berat_Badan 1 47.44 5.7e-12 ***
## HDL 1 23.56 1.2e-06 ***
## GD_Puasa 1 7.65 0.00568 **
## Kolestrol 1 8.38 0.00379 **
## Age 1 2.06 0.15150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
options(digits = 3)
beta = coef(log_model1)
OR = exp(beta)
cbind(beta, OR)
## beta OR
## (Intercept) -22.60617 1.52e-10
## Diastolik 0.13770 1.15e+00
## Nyeri_Dada 2.93876 1.89e+01
## Sistolik 0.03207 1.03e+00
## Lipoprotein 0.11754 1.12e+00
## Berat_Badan -0.10288 9.02e-01
## HDL -0.06236 9.40e-01
## GD_Puasa 0.00796 1.01e+00
## Kolestrol 0.00888 1.01e+00
## Age 0.01962 1.02e+00
pred5 <- predict(log_model1, test_dt, type = "response")
predicted <- round(pred5)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1429 25
## 1 27 116
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1429 25
## 1 27 116
##
## Accuracy : 0.967
## 95% CI : (0.958, 0.976)
## No Information Rate : 0.912
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.799
##
## Mcnemar's Test P-Value : 0.89
##
## Sensitivity : 0.981
## Specificity : 0.823
## Pos Pred Value : 0.983
## Neg Pred Value : 0.811
## Prevalence : 0.912
## Detection Rate : 0.895
## Detection Prevalence : 0.910
## Balanced Accuracy : 0.902
##
## 'Positive' Class : 0
##
# stepwise: automatic model selection method
options(warn=-1)
log_model_all <- glm(Target_PJK ~ ., family="binomial", data= smote_reglog)
log_model_nothing <- glm(Target_PJK ~ 1, family="binomial", data= smote_reglog)
log_model2 <- step(log_model_nothing,
list(lower=formula(log_model_nothing),
upper=formula(log_model_all)),
direction="both", trace = F, test= "F")
formula(log_model2)
## Target_PJK ~ Diastolik + Riwayat_Keluarga + Nyeri_Dada + Sistolik +
## GD_PP + Berat_Badan + Lipoprotein + HDL + LDL + Age + Merokok
log_model2 <- glm(Target_PJK ~ Diastolik + Riwayat_Keluarga + Nyeri_Dada + Sistolik +
GD_PP + Berat_Badan + Lipoprotein + HDL + LDL + Age + Merokok,
family = "binomial", data = smote_reglog)
summary(log_model2)
##
## Call:
## glm(formula = Target_PJK ~ Diastolik + Riwayat_Keluarga + Nyeri_Dada +
## Sistolik + GD_PP + Berat_Badan + Lipoprotein + HDL + LDL +
## Age + Merokok, family = "binomial", data = smote_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.879 -0.067 -0.001 0.009 2.935
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -24.97253 1.95399 -12.78 < 2e-16 ***
## Diastolik 0.14126 0.01871 7.55 4.3e-14 ***
## Riwayat_Keluarga1 19.57719 826.69837 0.02 0.98111
## Nyeri_Dada 3.41526 0.37020 9.23 < 2e-16 ***
## Sistolik 0.05544 0.01099 5.05 4.5e-07 ***
## GD_PP 0.00691 0.00216 3.20 0.00136 **
## Berat_Badan -0.13994 0.01789 -7.82 5.1e-15 ***
## Lipoprotein 0.13972 0.02084 6.70 2.0e-11 ***
## HDL -0.04850 0.01265 -3.83 0.00013 ***
## LDL 0.01000 0.00363 2.75 0.00590 **
## Age 0.02637 0.01526 1.73 0.08408 .
## Merokok 0.41872 0.29307 1.43 0.15308
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4158.88 on 2999 degrees of freedom
## Residual deviance: 485.33 on 2988 degrees of freedom
## AIC: 509.3
##
## Number of Fisher Scoring iterations: 20
log_model2 <- update(log_model2, .~. -Riwayat_Keluarga)
summary(log_model2)
##
## Call:
## glm(formula = Target_PJK ~ Diastolik + Nyeri_Dada + Sistolik +
## GD_PP + Berat_Badan + Lipoprotein + HDL + LDL + Age + Merokok,
## family = "binomial", data = smote_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.108 -0.074 -0.001 0.066 2.893
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -23.97114 1.68886 -14.19 < 2e-16 ***
## Diastolik 0.15416 0.01786 8.63 < 2e-16 ***
## Nyeri_Dada 3.20557 0.33882 9.46 < 2e-16 ***
## Sistolik 0.05379 0.01042 5.16 2.5e-07 ***
## GD_PP 0.00779 0.00209 3.72 0.0002 ***
## Berat_Badan -0.15026 0.01689 -8.90 < 2e-16 ***
## Lipoprotein 0.13493 0.01921 7.02 2.2e-12 ***
## HDL -0.06409 0.01196 -5.36 8.4e-08 ***
## LDL 0.01133 0.00326 3.48 0.0005 ***
## Age 0.02724 0.01413 1.93 0.0538 .
## Merokok 0.76164 0.26528 2.87 0.0041 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4158.88 on 2999 degrees of freedom
## Residual deviance: 599.49 on 2989 degrees of freedom
## AIC: 621.5
##
## Number of Fisher Scoring iterations: 8
GOF
library(ResourceSelection)
hoslem.test(log_model2$y, fitted(log_model2))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: log_model2$y, fitted(log_model2)
## X-squared = 14, df = 8, p-value = 0.08
Uji Simultan
library(pscl)
pR2(log_model2)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -299.743 -2079.442 3559.398 0.856 0.695 0.926
qchisq(0.95, 18)
## [1] 28.9
Uji Parsial
Anova (log_model2, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Diastolik 1 74.48 < 2e-16 ***
## Nyeri_Dada 1 89.51 < 2e-16 ***
## Sistolik 1 26.64 2.5e-07 ***
## GD_PP 1 13.86 0.0002 ***
## Berat_Badan 1 79.14 < 2e-16 ***
## Lipoprotein 1 49.32 2.2e-12 ***
## HDL 1 28.72 8.4e-08 ***
## LDL 1 12.11 0.0005 ***
## Age 1 3.72 0.0538 .
## Merokok 1 8.24 0.0041 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
options(digits = 3)
beta = coef(log_model2)
OR = exp(beta)
cbind(beta, OR)
## beta OR
## (Intercept) -23.97114 3.89e-11
## Diastolik 0.15416 1.17e+00
## Nyeri_Dada 3.20557 2.47e+01
## Sistolik 0.05379 1.06e+00
## GD_PP 0.00779 1.01e+00
## Berat_Badan -0.15026 8.60e-01
## Lipoprotein 0.13493 1.14e+00
## HDL -0.06409 9.38e-01
## LDL 0.01133 1.01e+00
## Age 0.02724 1.03e+00
## Merokok 0.76164 2.14e+00
pred6 <- predict(log_model2, test_dt, type = "response")
predicted <- round(pred6)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1398 8
## 1 58 133
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1398 8
## 1 58 133
##
## Accuracy : 0.959
## 95% CI : (0.948, 0.968)
## No Information Rate : 0.912
## P-Value [Acc > NIR] : 2.05e-13
##
## Kappa : 0.779
##
## Mcnemar's Test P-Value : 1.62e-09
##
## Sensitivity : 0.960
## Specificity : 0.943
## Pos Pred Value : 0.994
## Neg Pred Value : 0.696
## Prevalence : 0.912
## Detection Rate : 0.875
## Detection Prevalence : 0.880
## Balanced Accuracy : 0.952
##
## 'Positive' Class : 0
##
# stepwise: automatic model selection method
options(warn=-1)
log_model_all <- glm(Target_PJK ~ ., family="binomial", data= oversampling_reglog)
log_model_nothing <- glm(Target_PJK ~ 1, family="binomial", data= oversampling_reglog)
log_model3 <- step(log_model_nothing,
list(lower=formula(log_model_nothing),
upper=formula(log_model_all)),
direction="both", trace = F, test= "F")
formula(log_model3)
## Target_PJK ~ Diastolik + Riwayat_Keluarga + Sistolik + Nyeri_Dada +
## LDL + Target_Hipertensi + Lipoprotein + Berat_Badan + GD_PP +
## Age + Merokok + HDL + GD_Puasa
log_model3 <- glm(Target_PJK ~ Diastolik + Riwayat_Keluarga + Sistolik + Nyeri_Dada +
LDL + Target_Hipertensi + Lipoprotein + Berat_Badan + GD_PP +
Age + Merokok + HDL + GD_Puasa,
family = "binomial", data = oversampling_reglog)
summary(log_model3)
##
## Call:
## glm(formula = Target_PJK ~ Diastolik + Riwayat_Keluarga + Sistolik +
## Nyeri_Dada + LDL + Target_Hipertensi + Lipoprotein + Berat_Badan +
## GD_PP + Age + Merokok + HDL + GD_Puasa, family = "binomial",
## data = oversampling_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.658 -0.172 -0.023 0.029 3.624
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.73524 1.09663 -16.17 < 2e-16 ***
## Diastolik 0.07793 0.00871 8.95 < 2e-16 ***
## Riwayat_Keluarga1 20.16382 469.14090 0.04 0.9657
## Sistolik 0.03672 0.00527 6.97 3.1e-12 ***
## Nyeri_Dada 2.42049 0.22322 10.84 < 2e-16 ***
## LDL 0.01108 0.00211 5.24 1.6e-07 ***
## Target_Hipertensi1 1.64761 0.23830 6.91 4.7e-12 ***
## Lipoprotein 0.04792 0.00753 6.36 2.0e-10 ***
## Berat_Badan -0.03341 0.00679 -4.92 8.7e-07 ***
## GD_PP 0.00216 0.00125 1.73 0.0829 .
## Age 0.02118 0.00766 2.76 0.0057 **
## Merokok 0.32463 0.17520 1.85 0.0639 .
## HDL -0.00559 0.00301 -1.86 0.0632 .
## GD_Puasa 0.00322 0.00200 1.61 0.1075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5170.75 on 3729 degrees of freedom
## Residual deviance: 927.39 on 3716 degrees of freedom
## AIC: 955.4
##
## Number of Fisher Scoring iterations: 19
log_model3 <- update(log_model3, .~. -Riwayat_Keluarga)
summary(log_model3)
##
## Call:
## glm(formula = Target_PJK ~ Diastolik + Sistolik + Nyeri_Dada +
## LDL + Target_Hipertensi + Lipoprotein + Berat_Badan + GD_PP +
## Age + Merokok + HDL + GD_Puasa, family = "binomial", data = oversampling_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.831 -0.223 -0.031 0.117 3.489
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.04280 0.94236 -18.09 < 2e-16 ***
## Diastolik 0.08210 0.00766 10.71 < 2e-16 ***
## Sistolik 0.03702 0.00444 8.33 < 2e-16 ***
## Nyeri_Dada 2.20807 0.19391 11.39 < 2e-16 ***
## LDL 0.01184 0.00184 6.42 1.4e-10 ***
## Target_Hipertensi1 1.53198 0.20862 7.34 2.1e-13 ***
## Lipoprotein 0.04267 0.00667 6.40 1.6e-10 ***
## Berat_Badan -0.03538 0.00612 -5.78 7.5e-09 ***
## GD_PP 0.00314 0.00111 2.84 0.0045 **
## Age 0.01781 0.00674 2.64 0.0082 **
## Merokok 0.62949 0.14813 4.25 2.1e-05 ***
## HDL -0.00672 0.00261 -2.58 0.0099 **
## GD_Puasa 0.00307 0.00177 1.73 0.0833 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5170.7 on 3729 degrees of freedom
## Residual deviance: 1230.5 on 3717 degrees of freedom
## AIC: 1257
##
## Number of Fisher Scoring iterations: 7
GOF
library(ResourceSelection)
hoslem.test(log_model3$y, fitted(log_model3))
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: log_model3$y, fitted(log_model3)
## X-squared = 26, df = 8, p-value = 9e-04
Uji Simultan
library(pscl)
pR2(log_model3)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -615.254 -2585.374 3940.241 0.762 0.652 0.870
qchisq(0.95, 18)
## [1] 28.9
Uji Parsial
Anova (log_model3, type = 'II', test = 'Wald')
## Analysis of Deviance Table (Type II tests)
##
## Response: Target_PJK
## Df Chisq Pr(>Chisq)
## Diastolik 1 114.77 < 2e-16 ***
## Sistolik 1 69.46 < 2e-16 ***
## Nyeri_Dada 1 129.67 < 2e-16 ***
## LDL 1 41.23 1.4e-10 ***
## Target_Hipertensi 1 53.93 2.1e-13 ***
## Lipoprotein 1 40.90 1.6e-10 ***
## Berat_Badan 1 33.39 7.5e-09 ***
## GD_PP 1 8.08 0.0045 **
## Age 1 6.99 0.0082 **
## Merokok 1 18.06 2.1e-05 ***
## HDL 1 6.65 0.0099 **
## GD_Puasa 1 3.00 0.0833 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
options(digits = 3)
beta = coef(log_model3)
OR = exp(beta)
cbind(beta, OR)
## beta OR
## (Intercept) -17.04280 3.97e-08
## Diastolik 0.08210 1.09e+00
## Sistolik 0.03702 1.04e+00
## Nyeri_Dada 2.20807 9.10e+00
## LDL 0.01184 1.01e+00
## Target_Hipertensi1 1.53198 4.63e+00
## Lipoprotein 0.04267 1.04e+00
## Berat_Badan -0.03538 9.65e-01
## GD_PP 0.00314 1.00e+00
## Age 0.01781 1.02e+00
## Merokok 0.62949 1.88e+00
## HDL -0.00672 9.93e-01
## GD_Puasa 0.00307 1.00e+00
pred7 <- predict(log_model3, test_dt, type = "response")
predicted <- round(pred7)
tab <- table(Predicted = predicted, Reference = test_dt$Target_PJK)
tab
## Reference
## Predicted 0 1
## 0 1384 7
## 1 72 134
confusionMatrix(as.factor(predicted), test_dt$Target_PJK)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1384 7
## 1 72 134
##
## Accuracy : 0.951
## 95% CI : (0.939, 0.961)
## No Information Rate : 0.912
## P-Value [Acc > NIR] : 2.37e-09
##
## Kappa : 0.746
##
## Mcnemar's Test P-Value : 6.00e-13
##
## Sensitivity : 0.951
## Specificity : 0.950
## Pos Pred Value : 0.995
## Neg Pred Value : 0.650
## Prevalence : 0.912
## Detection Rate : 0.867
## Detection Prevalence : 0.871
## Balanced Accuracy : 0.950
##
## 'Positive' Class : 0
##
# Assuming you have the accuracy, sensitivity, specificity, and AIC values for each model
# Create a data frame to store the results
model_comparison <- data.frame(
Model = c("reglog_smote", "reglog_over", "reglog_under", "log_model1", "log_model2", "log_model3"),
Accuracy = c(0.962, 0.957, 0.966, 0.967, 0.959 , 0.951),
Sensitivity = c(0.964, 0.957, 0.966, 0.981, 0.960, 0.951),
Specificity = c(0.943, 0.957, 0.943, 0.823, 0.943, 0.950),
AIC = c(514.7, 958.1, 167.5, 401.4, 509.3, 1257)
)
# Fill in the AIC values for the models that have AIC available
model_comparison$AIC[6] <- AIC(log_model3)
# Print the model comparison table
print(model_comparison)
## Model Accuracy Sensitivity Specificity AIC
## 1 reglog_smote 0.962 0.964 0.943 515
## 2 reglog_over 0.957 0.957 0.957 958
## 3 reglog_under 0.966 0.966 0.943 168
## 4 log_model1 0.967 0.981 0.823 401
## 5 log_model2 0.959 0.960 0.943 509
## 6 log_model3 0.951 0.951 0.950 1257