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, unionlibrary(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(gtools)
library(gmodels)## Warning: package 'gmodels' was built under R version 4.2.3library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.3library(class)
library(tidyr)## Warning: package 'tidyr' was built under R version 4.2.3reglogbiner <- read_excel("D:/SEM 6/BISMILLAH LOMBA/SATDAT JUARA PART 2/Data fix/Data Cleaning_Hanung Safrizal.xlsx",
sheet = "Data Fix")## Warning: Expecting numeric in M2018 / R2018C13: got 'NA'head(reglogbiner)## # A tibble: 6 × 15
## Target_PJK Age Sex GD_Puasa GD_PP Kolestrol LDL HDL Lipoprotein
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 57 1 102 135 212 144 40 86.5
## 2 0 57 2 112 293 280 199 61 98
## 3 0 49 1 87 55 239 165 30 88
## 4 0 46 2 99 106 227 153 49 91
## 5 0 32 1 84 85 174 105 46 70.8
## 6 0 27 1 79 68 159 101 50 66.2
## # ℹ 6 more variables: Merokok <dbl>, Lingkar_Perut <dbl>,
## # Target_Hipertensi <dbl>, Nyeri_Dada <dbl>, Riwayat_Keluarga <chr>,
## # IMT <dbl>str(reglogbiner)## tibble [5,327 × 15] (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 32 27 40 35 38 27 ...
## $ Sex : num [1:5327] 1 2 1 2 1 1 1 2 1 2 ...
## $ GD_Puasa : num [1:5327] 102 112 87 99 84 79 82 89 88 78 ...
## $ GD_PP : num [1:5327] 135 293 55 106 85 68 121 109 99 143 ...
## $ Kolestrol : num [1:5327] 212 280 239 227 174 159 170 184 163 194 ...
## $ LDL : num [1:5327] 144 199 165 153 105 101 107 124 99 137 ...
## $ HDL : num [1:5327] 40 61 30 49 46 50 38 52 51 38 ...
## $ Lipoprotein : num [1:5327] 86.5 98 88 91 70.8 ...
## $ Merokok : num [1:5327] 3 3 4 3 4 4 4 0 0 4 ...
## $ Lingkar_Perut : num [1:5327] 86 97 84.5 95.5 78.5 84 84 84 79 84 ...
## $ Target_Hipertensi: num [1:5327] 0 1 0 1 1 0 0 0 0 1 ...
## $ Nyeri_Dada : num [1:5327] 0 0 0 0 1 0 0 0 0 0 ...
## $ Riwayat_Keluarga : chr [1:5327] "0" "0" "0" "0" ...
## $ IMT : num [1:5327] 27.4 29.1 24.7 32.4 22.2 ...# Pastikan tidak ada nilai NA di setiap kolom
reglogbiner <- na.omit(reglogbiner)
# Membentuk Data Frame
reglogbiner$Sex <- as.factor(reglogbiner$Sex)
reglogbiner$Target_PJK <- as.factor(reglogbiner$Target_PJK)
reglogbiner$Merokok <- as.factor(reglogbiner$Merokok)
reglogbiner$Target_Hipertensi<- as.factor(reglogbiner$Target_Hipertensi)
reglogbiner$Riwayat_Keluarga<- as.factor(reglogbiner$Riwayat_Keluarga)
# Periksa hasil
summary(reglogbiner)## Target_PJK Age Sex GD_Puasa GD_PP
## 0:4854 Min. :22.0 1:1787 Min. : 0.00 Min. : 0
## 1: 472 1st Qu.:35.0 2:3539 1st Qu.: 80.00 1st Qu.:100
## Median :44.0 Median : 85.00 Median :118
## Mean :43.7 Mean : 90.89 Mean :132
## 3rd Qu.:52.0 3rd Qu.: 92.00 3rd Qu.:143
## Max. :65.0 Max. :540.00 Max. :653
## Kolestrol LDL HDL Lipoprotein Merokok
## Min. : 0.0 Min. : 0.0 Min. : 0.00 Min. : 49.20 0:2252
## 1st Qu.:177.0 1st Qu.:107.0 1st Qu.:43.00 1st Qu.: 71.00 3:1196
## Median :200.0 Median :128.0 Median :50.00 Median : 80.30 4:1878
## Mean :202.6 Mean :129.2 Mean :50.41 Mean : 80.49
## 3rd Qu.:225.0 3rd Qu.:149.0 3rd Qu.:57.00 3rd Qu.: 88.50
## Max. :592.0 Max. :286.0 Max. :98.00 Max. :169.00
## Lingkar_Perut Target_Hipertensi Nyeri_Dada Riwayat_Keluarga
## Min. : 44.00 0:2931 Min. :0.0000 0:4587
## 1st Qu.: 79.00 1:2395 1st Qu.:0.0000 1: 739
## Median : 84.00 Median :0.0000
## Mean : 83.64 Mean :0.1388
## 3rd Qu.: 88.00 3rd Qu.:0.0000
## Max. :149.20 Max. :1.0000
## IMT
## Min. :13.90
## 1st Qu.:21.46
## Median :24.66
## Mean :24.90
## 3rd Qu.:27.72
## Max. :48.51# Menampilkan struktur data frame
str(reglogbiner)## tibble [5,326 × 15] (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 [1:5326] 57 57 49 46 32 27 40 35 38 27 ...
## $ Sex : Factor w/ 2 levels "1","2": 1 2 1 2 1 1 1 2 1 2 ...
## $ GD_Puasa : num [1:5326] 102 112 87 99 84 79 82 89 88 78 ...
## $ GD_PP : num [1:5326] 135 293 55 106 85 68 121 109 99 143 ...
## $ Kolestrol : num [1:5326] 212 280 239 227 174 159 170 184 163 194 ...
## $ LDL : num [1:5326] 144 199 165 153 105 101 107 124 99 137 ...
## $ HDL : num [1:5326] 40 61 30 49 46 50 38 52 51 38 ...
## $ Lipoprotein : num [1:5326] 86.5 98 88 91 70.8 ...
## $ Merokok : Factor w/ 3 levels "0","3","4": 2 2 3 2 3 3 3 1 1 3 ...
## $ Lingkar_Perut : num [1:5326] 86 97 84.5 95.5 78.5 84 84 84 79 84 ...
## $ Target_Hipertensi: Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 1 1 2 ...
## $ Nyeri_Dada : num [1:5326] 0 0 0 0 1 0 0 0 0 0 ...
## $ Riwayat_Keluarga : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ IMT : num [1:5326] 27.4 29.1 24.7 32.4 22.2 ...
## - attr(*, "na.action")= 'omit' Named int 2017
## ..- attr(*, "names")= chr "2017"pcheart <- table(reglogbiner$Target_PJK)
pcheart##
## 0 1
## 4854 472prop.table(table(reglogbiner$Target_PJK))##
## 0 1
## 0.91137814 0.08862186library(caret)## Loading required package: latticeset.seed(45) # digunakan untuk menghasilkan hasil acak yang sama setiap kali dijalankan
train_index <- createDataPartition(y = reglogbiner$Target_PJK, p = .7, list = FALSE) # memilih indeks secara acak untuk set pelatihan
train_reglog <- as.data.frame(reglogbiner[train_index, ]) # memilih data untuk set pelatihan
test_reglog <- as.data.frame(reglogbiner[-train_index, ]) # memilih data untuk set pengujian
train_reglog## Target_PJK Age Sex GD_Puasa GD_PP Kolestrol LDL HDL Lipoprotein Merokok
## 1 0 57 1 102 135 212 144 40 86.50000 3
## 2 0 57 2 112 293 280 199 61 98.00000 3
## 3 0 46 2 99 106 227 153 49 91.00000 3
## 4 0 27 1 79 68 159 101 50 66.20000 4
## 5 0 40 1 82 121 170 107 38 93.00000 4
## 6 0 35 2 89 109 184 124 52 86.00000 0
## 7 0 38 1 88 99 163 99 51 72.00000 0
## 8 0 27 2 78 143 194 137 38 90.00000 4
## 9 0 52 1 91 188 199 135 36 99.00000 3
## 10 0 25 1 87 104 161 104 46 81.00000 4
## 11 0 50 2 104 156 195 115 54 85.00000 4
## 12 0 30 2 94 117 184 82 86 76.00000 0
## 13 0 39 2 164 230 231 145 70 99.00000 4
## 14 0 45 1 102 106 180 117 31 89.00000 0
## 15 0 39 2 91 138 189 107 74 64.00000 3
## 16 0 46 1 88 85 207 142 44 73.00000 0
## 17 0 45 2 114 257 251 174 64 88.00000 4
## 18 0 37 2 85 99 140 75 58 104.00000 0
## 19 0 60 1 90 123 161 97 50 74.00000 0
## 20 0 55 2 83 129 240 142 94 83.00000 4
## 21 0 58 1 100 160 220 148 50 74.00000 3
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## 23 0 42 2 79 95 157 90 43 88.00000 3
## 24 0 45 1 119 90 225 160 45 69.00000 0
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## 26 0 42 1 84 92 204 124 44 78.50000 0
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## 28 0 26 2 86 97 222 150 40 80.00000 4
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## 3669 0 50 2 90 149 166 114 48 85.30000 0
## 3670 1 31 2 85 119 156 92 52 84.50000 0
## 3671 0 42 2 87 112 254 165 75 61.20000 0
## 3672 0 36 1 209 370 172 131 36 90.50000 0
## 3673 0 36 2 87 93 154 84 59 70.90000 0
## 3674 0 37 2 102 108 178 106 61 74.80000 0
## 3675 0 25 1 80 88 147 98 40 69.80000 0
## 3676 0 30 2 85 118 203 129 50 80.00000 0
## 3677 0 45 2 92 121 153 98 53 89.20000 4
## 3678 0 39 2 84 98 202 127 63 80.10000 0
## 3679 0 34 2 85 104 194 130 50 89.30000 0
## 3680 0 32 2 91 124 226 163 49 72.70000 0
## 3681 0 31 2 98 154 196 114 46 89.60000 0
## 3682 0 38 2 85 118 203 129 50 80.00000 0
## 3683 0 32 2 88 131 213 139 56 82.20000 3
## 3684 0 45 1 88 155 242 190 46 91.30000 4
## 3685 0 42 2 82 128 201 126 64 74.60000 0
## 3686 0 30 2 107 270 304 242 46 87.50000 4
## 3687 0 32 2 105 179 154 87 53 113.50000 4
## 3688 0 41 2 100 117 171 102 56 94.20000 4
## 3689 0 35 2 93 192 205 160 41 104.70000 0
## 3690 0 46 2 92 145 248 202 46 92.30000 4
## 3691 0 44 1 83 107 168 93 57 73.20000 0
## 3692 0 43 2 84 115 153 84 64 73.20000 3
## 3693 0 49 2 154 252 171 104 53 79.60000 0
## 3694 1 35 2 83 135 141 89 37 89.10000 3
## 3695 0 33 1 87 91 190 156 33 84.00000 0
## 3696 0 26 2 89 117 128 81 46 85.00000 4
## 3697 0 26 1 87 108 165 89 28 93.00000 4
## 3698 0 26 2 83 105 239 160 64 80.00000 0
## 3699 1 50 2 93 118 212 126 73 97.00000 4
## 3700 0 47 2 102 139 155 68 67 63.00000 4
## 3701 0 46 2 84 152 195 128 60 96.50000 4
## 3702 0 26 2 85 118 203 129 50 80.00000 3
## 3703 1 26 2 85 118 203 129 50 80.00000 4
## 3704 0 42 2 91 100 187 132 44 88.00000 0
## 3705 1 26 2 89 137 152 87 56 80.00000 3
## 3706 0 33 2 85 107 150 95 55 78.00000 0
## 3707 0 46 2 84 111 191 129 44 90.50000 0
## 3708 0 25 2 73 101 162 96 53 79.00000 0
## 3709 0 34 2 103 114 217 153 59 92.50000 4
## 3710 0 33 2 81 96 145 87 48 85.00000 0
## 3711 0 32 2 88 126 210 126 57 87.00000 0
## 3712 0 27 2 80 147 167 98 61 79.00000 0
## 3713 0 46 1 76 124 177 88 33 97.00000 0
## 3714 0 39 2 87 111 204 120 68 88.00000 4
## 3715 0 32 2 85 100 183 119 44 94.50000 4
## 3716 0 43 2 92 107 194 109 76 73.00000 4
## 3717 0 39 2 84 128 189 115 53 87.50000 0
## 3718 0 36 1 87 84 216 138 68 73.50000 0
## 3719 0 33 2 94 133 186 56 34 93.50000 0
## 3720 1 38 2 72 169 148 85 55 92.00000 0
## 3721 0 27 2 87 139 174 126 34 85.50000 4
## 3722 0 47 1 86 155 206 149 36 84.00000 0
## 3723 0 46 2 176 284 249 183 52 95.00000 0
## 3724 0 30 2 81 125 144 85 46 79.00000 0
## 3725 0 57 2 88 129 168 115 53 79.50000 0
## 3726 0 42 1 108 143 176 105 58 82.00000 4
## 3727 0 35 2 86 113 238 168 47 97.00000 0
## 3728 0 37 1 83 105 221 153 43 93.60000 0
## 3729 0 30 2 83 122 189 123 50 79.20000 0
## Lingkar_Perut Target_Hipertensi Nyeri_Dada Riwayat_Keluarga IMT
## 1 86.0 0 0 0 27.43844
## 2 97.0 1 0 0 29.12806
## 3 95.5 1 0 0 32.35673
## 4 84.0 0 0 0 18.59012
## 5 84.0 0 0 0 24.74094
## 6 84.0 0 0 0 25.63117
## 7 79.0 0 0 0 20.70082
## 8 84.0 1 0 0 29.20116
## 9 84.0 1 0 0 29.38476
## 10 84.0 0 0 0 21.33821
## 11 82.0 0 1 1 22.36744
## 12 73.0 1 0 0 21.75547
## 13 97.6 1 0 0 35.11111
## 14 84.0 0 0 0 25.84777
## 15 78.0 0 0 0 18.76525
## 16 73.0 0 0 0 19.60716
## 17 83.0 0 0 0 26.40716
## 18 89.0 1 0 0 30.54283
## 19 84.0 0 0 0 20.57613
## 20 94.0 1 1 1 25.91513
## 21 84.0 0 0 0 20.02884
## 22 84.0 0 0 0 23.28342
## 23 87.0 1 0 0 25.31855
## 24 84.0 0 0 0 19.81405
## 25 84.0 0 0 0 25.65263
## 26 84.0 0 0 0 23.24459
## 27 80.8 1 0 0 26.93572
## 28 84.0 1 0 0 21.22789
## 29 84.0 0 0 0 24.21875
## 30 84.0 1 1 1 37.53049
## 31 84.0 1 0 0 22.02348
## 32 99.0 0 0 0 29.89969
## 33 86.0 0 0 0 27.26801
## 34 70.0 1 0 0 19.09051
## 35 80.0 0 1 1 22.14533
## 36 101.8 0 0 0 27.82931
## 37 80.5 1 0 0 26.83518
## 38 93.0 1 0 0 28.22839
## 39 75.0 0 0 0 18.46764
## 40 84.0 1 0 0 25.91068
## 41 84.0 0 0 0 22.00597
## 42 89.0 1 0 0 25.10388
## 43 84.0 1 1 1 23.44923
## 44 74.0 1 0 0 19.94321
## 45 85.0 0 0 0 26.83518
## 46 80.0 1 0 0 22.34778
## 47 84.0 1 1 1 21.00073
## 48 84.0 0 0 0 16.44737
## 49 84.0 1 0 0 22.67574
## 50 93.0 0 1 1 26.77593
## 51 84.0 0 0 0 22.49135
## 52 78.0 0 0 0 25.23634
## 53 102.0 1 0 0 33.29370
## 54 84.0 0 0 0 27.29322
## 55 78.0 1 0 0 20.28480
## 56 99.0 1 0 0 32.85668
## 57 83.0 1 1 1 24.69041
## 58 84.0 1 0 0 26.57538
## 59 90.0 1 0 0 26.66667
## 60 75.0 0 0 0 20.93212
## 61 67.0 1 0 0 17.99816
## 62 84.0 0 0 0 28.23342
## 63 98.0 1 0 0 26.23356
## 64 88.0 1 0 0 29.48870
## 65 74.0 0 0 0 17.98167
## 66 84.0 0 0 0 44.37798
## 67 69.0 1 1 1 18.08021
## 68 90.0 0 1 1 23.60128
## 69 96.0 0 0 0 29.01308
## 70 78.0 1 1 1 23.73996
## 71 92.0 1 1 1 26.81359
## 72 84.0 0 0 0 24.45606
## 73 84.0 0 0 0 28.36325
## 74 75.0 1 0 0 22.07108
## 75 84.0 1 0 0 20.44674
## 76 67.0 1 0 0 18.81892
## 77 70.0 0 0 0 17.85062
## 78 82.0 1 0 0 25.00000
## 79 84.0 0 1 1 17.85360
## 80 84.0 0 0 0 20.26936
## 81 84.0 1 1 1 35.55556
## 82 62.0 1 1 1 17.06556
## 83 60.0 0 0 0 19.23438
## 84 63.0 1 0 0 17.96875
## 85 76.5 0 0 0 25.00000
## 86 82.0 0 0 0 21.09375
## 87 84.0 0 0 0 26.15563
## 88 65.0 1 0 0 18.79619
## 89 86.5 0 1 1 29.15452
## 90 84.0 1 0 0 21.33821
## 91 89.0 1 0 0 31.88592
## 92 84.0 1 0 0 20.30000
## 93 84.0 0 0 0 26.29849
## 94 84.0 0 0 0 24.05693
## 95 84.0 0 0 0 35.58398
## 96 73.0 1 0 0 25.10000
## 97 88.0 0 0 0 25.96953
## 98 84.0 1 0 0 23.30559
## 99 84.0 0 1 1 24.60973
## 100 84.0 1 0 0 22.40000
## 101 80.0 0 0 0 21.20000
## 102 83.0 1 0 0 21.69246
## 103 80.0 0 0 0 26.63496
## 104 85.0 0 0 0 27.58621
## 105 105.0 0 0 0 34.94943
## 106 85.0 1 0 0 24.79667
## 107 78.0 1 1 1 21.17022
## 108 68.0 1 0 0 19.85043
## 109 79.0 0 0 0 22.31328
## 110 84.0 0 0 0 21.44643
## 111 94.0 0 0 0 28.47989
## 112 84.0 1 0 0 24.23411
## 113 84.0 0 0 0 19.39646
## 114 80.0 0 0 0 23.08125
## 115 85.0 0 0 0 28.61700
## 116 77.0 1 0 0 25.70730
## 117 98.5 0 0 0 30.85938
## 118 76.0 1 0 0 23.37258
## 119 80.0 0 0 0 24.00000
## 120 79.0 1 0 0 29.82325
## 121 70.0 0 0 0 18.85882
## 122 67.0 0 0 0 19.48696
## 123 78.0 1 0 0 23.71184
## 124 68.0 0 0 0 19.23438
## 125 85.0 0 1 1 23.71184
## 126 70.0 0 0 0 19.70351
## 127 69.0 0 0 0 22.80602
## 128 93.0 1 1 1 27.00617
## 129 103.0 1 1 1 27.34232
## 130 83.0 0 0 0 27.84879
## 131 84.0 0 0 0 29.94036
## 132 73.0 0 0 0 24.52682
## 133 84.0 0 0 0 17.51463
## 134 82.0 0 1 1 26.91372
## 135 84.0 1 0 0 17.41907
## 136 73.2 0 0 0 18.91807
## 137 77.0 1 0 0 17.64706
## 138 90.0 0 0 0 33.75437
## 139 84.0 0 0 0 24.23823
## 140 84.0 1 0 0 23.50781
## 141 83.0 1 1 1 24.14152
## 142 89.0 0 0 0 26.93572
## 143 84.0 0 1 1 22.65625
## 144 61.0 0 0 0 18.13122
## 145 88.0 0 0 0 23.83300
## 146 95.0 1 1 1 28.30385
## 147 86.0 1 0 0 34.70776
## 148 89.0 1 0 0 23.82812
## 149 84.0 1 1 1 21.75547
## 150 75.0 1 0 0 23.82418
## 151 73.0 0 0 0 23.19109
## 152 89.0 0 1 1 27.41311
## 153 99.0 1 0 0 31.43179
## 154 87.0 1 0 0 19.74087
## 155 93.0 0 0 0 25.92593
## 156 84.0 0 0 0 28.13366
## 157 91.0 1 0 0 24.97704
## 158 107.0 1 0 0 32.71862
## 159 87.0 1 0 0 26.62000
## 160 69.0 0 1 1 19.10009
## 161 66.0 1 0 0 20.17264
## 162 84.0 0 0 0 18.90000
## 163 96.0 0 0 0 30.22222
## 164 65.0 0 0 0 18.76525
## 165 83.0 0 0 0 30.36000
## 166 86.0 0 0 0 23.97959
## 167 83.0 0 0 0 26.27135
## 168 71.0 0 0 0 20.36189
## 169 84.0 0 0 0 24.97704
## 170 80.0 1 0 0 27.00617
## 171 84.0 0 0 0 27.43484
## 172 64.0 0 0 0 19.56086
## 173 60.0 0 0 0 16.02307
## 174 73.0 0 1 1 21.45727
## 175 75.0 1 0 0 22.21368
## 176 68.0 0 0 0 21.71925
## 177 78.0 0 0 0 19.62826
## 178 99.0 1 0 0 35.45645
## 179 86.0 0 0 0 25.84777
## 180 75.0 1 1 1 25.29260
## 181 99.0 1 0 0 28.06894
## 182 84.0 1 0 0 17.21109
## 183 90.0 1 0 0 30.25193
## 184 84.0 1 0 0 28.19428
## 185 69.0 0 1 1 18.97453
## 186 74.0 0 1 1 21.09375
## 187 83.0 1 1 1 30.83208
## 188 82.0 1 0 0 23.23346
## 189 80.0 0 0 0 24.45107
## 190 87.0 0 0 0 31.68855
## 191 84.0 1 0 0 20.95717
## 192 84.0 0 0 0 27.70408
## 193 84.0 1 0 0 23.95556
## 194 80.0 0 0 0 26.57325
## 195 83.0 0 0 0 26.29849
## 196 73.0 1 0 0 20.63659
## 197 84.0 0 0 0 25.71661
## 198 84.0 0 0 0 23.87410
## 199 85.0 1 0 0 24.12175
## 200 61.0 1 0 0 16.88872
## 201 92.0 0 0 0 27.07922
## 202 73.0 1 1 1 20.13478
## 203 85.0 0 0 0 29.08613
## 204 84.0 0 0 0 18.21000
## 205 84.0 1 1 1 26.74000
## 206 78.0 0 0 0 31.36063
## 207 83.0 0 0 0 23.04191
## 208 84.0 0 0 0 23.47654
## 209 84.0 1 0 0 27.63492
## 210 84.0 0 0 0 23.06472
## 211 72.0 0 0 0 21.35991
## 212 83.0 1 0 0 25.06575
## 213 79.0 1 0 0 27.87422
## 214 90.0 1 0 0 25.87606
## 215 80.0 1 0 0 26.93572
## 216 84.0 1 0 0 28.75519
## 217 84.0 0 1 1 31.64432
## 218 91.0 0 0 0 32.88889
## 219 83.0 1 1 1 20.64177
## 220 81.0 1 0 0 25.68371
## 221 88.0 1 0 0 25.51020
## 222 78.0 1 0 0 26.78040
## 223 64.0 1 0 0 18.17867
## 224 54.0 1 0 0 13.89842
## 225 75.0 0 1 1 25.67452
## 226 84.0 1 1 1 20.23950
## 227 90.0 1 0 0 26.22693
## 228 72.0 0 0 0 23.13851
## 229 93.0 0 1 1 29.48870
## 230 94.0 1 0 0 28.12500
## 231 80.8 0 0 0 25.55941
## 232 74.0 1 0 0 20.04321
## 233 90.0 0 0 0 27.39226
## 234 83.0 0 0 0 23.30905
## 235 84.0 0 0 0 24.08822
## 236 81.1 0 0 0 22.07410
## 237 78.0 0 0 0 25.15315
## 238 85.0 0 0 0 26.83518
## 239 88.0 0 0 0 28.04038
## 240 86.0 1 1 1 26.12861
## 241 80.0 1 0 0 27.92667
## 242 75.0 1 0 0 24.88889
## 243 98.0 0 0 0 28.06894
## 244 72.0 1 1 1 21.71925
## 245 85.0 0 0 0 27.20961
## 246 85.0 0 0 0 26.93572
## 247 73.0 1 0 0 20.73688
## 248 77.0 1 0 0 26.66667
## 249 84.0 0 0 0 29.64269
## 250 90.0 1 0 0 28.30385
## 251 98.0 1 1 1 29.75758
## 252 84.0 1 0 0 30.05985
## 253 84.0 0 0 0 29.27796
## 254 107.0 0 0 0 34.89648
## 255 86.0 0 0 0 24.55775
## 256 61.0 1 0 0 18.26150
## 257 67.0 0 0 0 19.14062
## 258 77.0 0 0 0 22.21297
## 259 79.0 0 0 0 21.19274
## 260 69.0 0 1 1 23.80480
## 261 92.0 1 1 1 24.84098
## 262 80.0 1 0 0 25.88757
## 263 84.0 1 1 1 31.73973
## 264 106.0 0 1 1 36.40191
## 265 97.0 1 0 0 32.54722
## 266 84.0 1 0 0 23.73866
## 267 79.0 0 1 1 23.14815
## 268 99.0 0 0 0 32.79321
## 269 92.0 1 0 0 29.15452
## 270 84.0 1 0 0 22.98540
## 271 72.0 0 0 0 18.18000
## 272 70.0 0 0 0 22.67995
## 273 103.0 0 1 1 37.65465
## 274 84.0 1 0 0 25.76571
## 275 104.0 0 1 1 33.12131
## 276 83.0 1 0 0 24.16350
## 277 70.0 0 0 0 19.55556
## 278 101.5 0 1 1 35.38491
## 279 98.0 1 1 1 26.52035
## 280 96.0 1 0 0 28.34934
## 281 74.0 1 1 1 22.07410
## 282 90.0 0 0 0 30.13000
## 283 89.0 1 0 0 27.12032
## 284 89.0 0 0 0 29.96878
## 285 79.0 1 0 0 24.44444
## 286 73.0 1 0 0 22.77319
## 287 78.0 0 0 0 22.25078
## 288 87.0 0 0 0 26.34649
## 289 84.0 1 1 1 26.02264
## 290 69.0 0 0 0 21.50189
## 291 103.0 0 0 0 40.95444
## 292 89.5 0 0 0 30.49353
## 293 84.0 0 0 0 25.28025
## 294 101.0 0 1 1 38.73045
## 295 84.0 1 0 0 19.94755
## 296 83.0 0 1 1 24.05562
## 297 84.5 1 0 0 34.24618
## 298 72.0 1 0 0 22.56857
## 299 102.0 0 0 0 28.85888
## 300 95.0 1 1 1 27.73219
## 301 70.0 0 0 0 25.16055
## 302 96.0 1 0 0 28.48837
## 303 80.0 1 0 0 23.43900
## 304 92.0 1 1 1 27.65755
## 305 83.0 0 0 0 27.41940
## 306 114.0 1 0 0 37.04493
## 307 70.0 0 1 1 23.29308
## 308 85.0 1 0 0 32.77903
## 309 72.0 1 1 1 21.30159
## 310 90.0 1 0 0 27.69436
## 311 93.0 1 0 0 26.62619
## 312 86.0 0 1 1 27.83342
## 313 92.0 0 0 0 27.97919
## 314 96.0 0 0 0 24.85066
## 315 84.0 1 0 0 21.68655
## 316 67.0 0 0 0 20.05438
## 317 84.0 0 0 0 30.54555
## 318 80.8 0 0 0 21.72201
## 319 84.0 0 0 0 23.73321
## 320 88.0 0 0 0 25.81129
## 321 76.0 0 0 0 23.65872
## 322 84.0 0 0 0 22.75102
## 323 97.0 1 0 0 29.08505
## 324 94.0 0 1 1 27.23101
## 325 83.0 0 0 0 27.41674
## 326 70.0 1 0 0 25.36357
## 327 84.0 0 0 0 29.92309
## 328 84.0 1 1 1 25.90998
## 329 69.0 1 0 0 22.44063
## 330 99.0 0 0 0 29.94638
## 331 63.0 0 1 1 18.36062
## 332 81.0 1 0 0 22.55370
## 333 80.0 0 0 0 25.91992
## 334 84.0 1 0 0 25.14879
## 335 71.0 0 0 0 18.01302
## 336 83.0 0 1 1 21.08684
## 337 104.0 0 1 1 39.14168
## 338 73.0 0 1 1 24.53392
## 339 69.0 0 0 0 22.31700
## 340 94.0 0 0 0 27.17456
## 341 89.0 1 0 0 29.57614
## 342 98.0 0 0 0 30.84284
## 343 84.0 1 1 1 19.42924
## 344 84.0 0 0 0 25.00000
## 345 82.0 1 0 0 25.64883
## 346 84.0 0 0 0 17.53674
## 347 100.0 0 0 0 32.14124
## 348 99.0 1 0 0 31.16687
## 349 74.0 1 0 0 26.05674
## 350 81.0 1 0 0 26.83010
## 351 60.0 1 1 1 14.39215
## 352 84.0 0 1 1 22.90951
## 353 78.0 1 1 1 22.99357
## 354 91.0 0 0 0 28.07066
## 355 82.0 1 1 1 25.00693
## 356 80.0 0 0 0 27.40766
## 357 67.0 0 0 0 18.99525
## 358 84.0 0 0 0 21.02385
## 359 80.0 0 0 0 25.93025
## 360 99.0 0 0 0 32.63166
## 361 84.0 0 0 0 33.69090
## 362 84.0 1 1 1 25.71932
## 363 88.0 0 0 0 25.51805
## 364 84.0 1 0 0 24.58092
## 365 98.0 1 1 1 28.79815
## 366 74.0 0 0 0 21.11585
## 367 75.0 0 0 0 20.66976
## 368 84.0 0 0 0 18.71094
## 369 84.0 0 0 0 24.97501
## 370 84.0 0 0 0 22.19196
## 371 84.0 0 1 1 24.98736
## 372 84.0 0 0 0 19.90867
## 373 75.0 0 0 0 22.68438
## 374 87.0 0 0 0 27.18655
## 375 84.0 1 0 0 25.82968
## 376 66.0 1 0 0 22.16288
## 377 84.0 1 0 0 17.95221
## 378 73.0 0 0 0 34.43423
## 379 84.0 1 1 1 21.21832
## 380 69.0 1 1 1 20.82000
## 381 98.0 0 0 0 30.11999
## 382 92.0 1 0 0 28.55773
## 383 67.0 1 0 0 22.01046
## 384 83.0 1 0 0 27.89351
## 385 85.0 0 0 0 26.47045
## 386 83.0 1 1 1 22.65799
## 387 94.0 1 0 0 32.39001
## 388 79.0 1 1 1 22.83730
## 389 87.0 0 0 0 26.69804
## 390 90.0 1 0 0 24.99153
## 391 75.0 1 0 0 24.77210
## 392 77.0 1 0 0 20.06095
## 393 68.0 1 0 0 18.54935
## 394 59.0 1 0 0 17.35630
## 395 73.0 0 0 0 21.22714
## 396 90.0 1 1 1 29.27337
## 397 84.0 0 0 0 31.85596
## 398 84.0 1 0 0 29.31535
## 399 96.0 0 0 0 30.42720
## 400 81.0 1 0 0 26.13920
## 401 87.0 0 0 0 24.13223
## 402 79.0 0 0 0 18.67218
## 403 93.0 0 0 0 29.93362
## 404 86.0 1 0 0 25.27330
## 405 59.0 0 1 1 15.80794
## 406 91.0 1 0 0 23.73561
## 407 84.0 0 0 0 22.46364
## 408 82.0 0 0 0 22.07828
## 409 60.0 1 0 0 17.29403
## 410 75.0 1 1 1 19.90233
## 411 82.0 0 0 0 25.42587
## 412 68.0 0 0 0 20.21281
## 413 84.0 0 0 0 19.35000
## 414 84.0 0 0 0 16.99689
## 415 89.0 0 0 0 26.77900
## 416 76.0 1 0 0 21.37778
## 417 84.0 1 0 0 16.71121
## 418 84.0 0 1 1 20.41522
## 419 84.0 0 1 1 27.54870
## 420 86.0 0 0 0 24.27000
## 421 67.0 1 0 0 20.21047
## 422 54.0 0 0 0 14.23891
## 423 87.0 0 0 0 24.52682
## 424 71.0 0 0 0 19.79275
## 425 77.0 0 0 0 23.98589
## 426 84.0 0 0 0 18.44000
## 427 60.0 0 0 0 14.44444
## 428 67.0 0 0 0 20.09369
## 429 80.0 0 0 0 25.54152
## 430 75.0 1 0 0 21.93772
## 431 64.0 0 0 0 19.50296
## 432 88.0 0 0 0 26.27084
## 433 84.0 1 1 1 20.57683
## 434 84.0 1 0 0 29.15555
## 435 90.0 1 0 0 28.97664
## 436 84.0 1 0 0 29.52251
## 437 58.0 0 0 0 22.90951
## 438 87.5 0 0 0 25.00000
## 439 80.0 0 0 0 20.58143
## 440 84.0 1 0 0 20.33406
## 441 76.0 1 0 0 20.52764
## 442 84.0 0 0 0 32.21644
## 443 89.0 0 0 0 30.53917
## 444 84.0 0 0 0 23.24016
## 445 88.0 0 0 0 24.80448
## 446 84.0 1 0 0 19.15767
## 447 79.5 0 0 0 25.89631
## 448 75.0 1 0 0 21.08281
## 449 91.0 0 0 0 27.93766
## 450 81.0 0 0 0 23.22270
## 451 75.0 0 0 0 23.09099
## 452 84.0 0 1 1 25.02231
## 453 87.0 1 0 0 21.44970
## 454 66.0 1 0 0 19.45877
## 455 78.0 0 0 0 21.04592
## 456 82.0 0 0 0 24.42354
## 457 72.0 1 0 0 23.17657
## 458 95.0 0 0 0 28.99060
## 459 88.0 1 1 1 30.01129
## 460 76.0 1 0 0 25.68371
## 461 62.0 1 0 0 17.33701
## 462 89.0 0 0 0 28.80465
## 463 84.0 0 1 1 25.46401
## 464 74.0 0 0 0 23.31349
## 465 69.0 1 0 0 18.43021
## 466 84.0 0 0 0 27.03000
## 467 84.0 1 1 1 24.70000
## 468 84.0 1 0 0 41.33175
## 469 86.0 1 0 0 26.14133
## 470 78.0 1 0 0 24.35126
## 471 76.0 0 0 0 23.66963
## 472 89.0 0 0 0 29.79505
## 473 76.0 1 0 0 20.71594
## 474 102.0 0 0 0 31.20600
## 475 84.0 0 0 0 26.17899
## 476 110.0 0 0 0 38.90000
## 477 84.0 0 1 1 20.10000
## 478 83.0 0 1 1 30.24249
## 479 80.0 1 0 0 26.54237
## 480 76.0 1 1 1 26.40410
## 481 84.0 1 0 0 21.42486
## 482 84.0 0 0 0 29.66000
## 483 79.0 1 0 0 24.26514
## 484 68.0 0 1 1 19.56744
## 485 93.0 1 0 0 32.35673
## 486 106.0 0 1 1 32.00590
## 487 84.0 0 0 0 29.98649
## 488 86.0 1 0 0 23.91296
## 489 85.0 1 0 0 23.74402
## 490 84.0 1 0 0 17.91232
## 491 92.0 0 0 0 28.78499
## 492 98.0 1 0 0 30.74888
## 493 88.0 1 1 1 27.51545
## 494 87.0 1 1 1 22.89824
## 495 84.0 1 0 0 14.30238
## 496 91.0 1 0 0 26.93603
## 497 78.0 1 0 0 23.84468
## 498 104.0 0 0 0 41.63439
## 499 83.0 0 0 0 23.63363
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## 1634 55.0 0 0 0 14.24064
## 1635 84.0 0 0 0 23.87511
## 1636 89.0 0 0 0 27.24444
## 1637 85.0 0 0 0 24.88426
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## 1640 80.0 1 0 0 21.04308
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## 1643 74.0 1 0 0 18.06737
## 1644 69.0 1 0 0 21.14808
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## 1660 81.0 0 0 0 23.48600
## 1661 84.0 1 0 0 25.17134
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## 1668 84.0 0 0 0 19.26609
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## 1671 72.0 0 1 1 19.96550
## 1672 84.0 0 0 0 16.44815
## 1673 84.0 0 0 0 21.77778
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## 1675 84.0 0 0 0 22.14815
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## 1677 84.0 1 0 0 20.63905
## 1678 67.0 0 0 0 20.05609
## 1679 117.0 0 0 0 36.70741
## 1680 84.0 0 0 0 27.23519
## 1681 82.0 1 1 1 24.83765
## 1682 81.0 1 0 0 22.05576
## 1683 90.0 0 0 0 28.87092
## 1684 104.0 0 1 1 35.93174
## 1685 90.0 1 0 0 26.46105
## 1686 77.0 1 0 0 19.03161
## 1687 65.0 1 0 0 16.55828
## 1688 84.0 0 0 0 18.80126
## 1689 95.0 0 0 0 25.40608
## 1690 67.0 0 0 0 19.97498
## 1691 84.0 0 0 0 26.91779
## 1692 83.0 1 0 0 26.81839
## 1693 87.0 0 0 0 29.50835
## 1694 95.0 1 0 0 32.00000
## 1695 90.0 1 1 1 21.43257
## 1696 75.0 0 0 0 18.22099
## 1697 84.0 1 1 1 19.71277
## 1698 100.0 1 0 0 28.35043
## 1699 87.5 0 0 0 29.48496
## 1700 84.0 1 0 0 26.00834
## 1701 84.0 0 0 0 19.81657
## 1702 84.0 1 0 0 23.20720
## 1703 84.0 0 0 0 23.34694
## 1704 84.0 0 0 0 21.91572
## 1705 84.0 1 0 0 28.59172
## 1706 84.0 0 0 0 20.71006
## 1707 78.0 0 0 0 24.10838
## 1708 84.0 0 0 0 22.97793
## 1709 84.0 1 0 0 25.40299
## 1710 84.0 1 0 0 20.05385
## 1711 93.0 0 0 0 27.24032
## 1712 69.0 0 0 0 21.35074
## 1713 84.0 0 0 0 23.13438
## 1714 82.5 0 0 0 24.77961
## 1715 84.0 0 0 0 25.70684
## 1716 84.0 0 0 0 24.41970
## 1717 91.1 0 0 0 29.80112
## 1718 84.0 0 0 0 19.35441
## 1719 88.0 1 1 1 26.03935
## 1720 92.0 0 0 0 26.53298
## 1721 81.0 1 0 0 28.50544
## 1722 94.0 0 0 0 29.47090
## 1723 84.0 0 0 0 25.00000
## 1724 94.5 0 0 0 27.99308
## 1725 84.0 1 1 1 26.88496
## 1726 84.0 0 0 0 29.84961
## 1727 79.0 0 0 0 26.38411
## 1728 83.0 0 0 0 26.35572
## 1729 79.0 0 0 0 24.12818
## 1730 85.5 0 0 0 27.14490
## 1731 84.0 1 0 0 30.57409
## 1732 77.0 1 0 0 22.70408
## 1733 113.0 1 0 0 35.15228
## 1734 67.0 0 0 0 19.36310
## 1735 66.0 1 0 0 18.39349
## 1736 78.0 1 0 0 27.96022
## 1737 60.0 1 0 0 20.47298
## 1738 99.0 1 0 0 31.63006
## 1739 79.0 0 0 0 18.69212
## 1740 83.0 1 0 0 22.25263
## 1741 60.0 0 0 0 19.19478
## 1742 84.0 0 0 0 28.92467
## 1743 84.0 0 0 0 25.60184
## 1744 82.0 1 0 0 25.69851
## 1745 84.0 1 0 0 16.91954
## 1746 84.0 0 1 1 24.39660
## 1747 84.0 0 0 0 23.90712
## 1748 84.0 1 0 0 19.63253
## 1749 84.0 1 0 0 25.00000
## 1750 84.0 0 1 1 19.14782
## 1751 84.0 0 0 0 22.46128
## 1752 96.0 0 0 0 23.69074
## 1753 95.0 0 0 0 26.63285
## 1754 91.0 0 0 0 27.66174
## 1755 84.0 0 0 0 25.58821
## 1756 87.5 0 0 0 27.89244
## 1757 97.0 0 0 0 28.87297
## 1758 84.0 1 0 0 22.74435
## 1759 84.0 0 0 0 28.21429
## 1760 72.0 0 0 0 20.03192
## 1761 84.0 1 1 1 17.40022
## 1762 84.0 0 0 0 16.10759
## 1763 84.0 0 0 0 15.19457
## 1764 88.0 1 0 0 24.86769
## 1765 90.0 0 0 0 30.88541
## 1766 84.0 1 0 0 26.07924
## 1767 84.0 1 0 0 27.21863
## 1768 84.0 1 0 0 25.35869
## 1769 84.0 1 0 0 32.76444
## 1770 90.0 1 1 1 26.54116
## 1771 84.0 0 0 0 21.69093
## 1772 84.0 0 0 0 27.78696
## 1773 84.0 0 0 0 16.68934
## 1774 84.0 0 0 0 27.57632
## 1775 93.0 1 0 0 26.39407
## 1776 83.0 0 0 0 24.36417
## 1777 84.0 1 0 0 19.74051
## 1778 73.0 0 0 0 20.56547
## 1779 44.0 0 0 0 18.37379
## 1780 72.0 0 0 0 22.33219
## 1781 82.5 0 0 0 28.88933
## 1782 84.0 0 0 0 22.22656
## 1783 84.0 1 0 0 17.57812
## 1784 87.0 0 0 0 28.21654
## 1785 89.0 1 0 0 24.98145
## 1786 84.0 1 0 0 24.64844
## 1787 60.0 0 0 0 17.24208
## 1788 84.0 0 0 0 31.89230
## 1789 84.0 0 0 0 16.10084
## 1790 84.0 0 0 0 21.77041
## 1791 84.0 1 0 0 24.50340
## 1792 88.0 1 0 0 29.46144
## 1793 94.0 0 0 0 33.19670
## 1794 84.0 1 0 0 18.70496
## 1795 84.0 0 1 1 22.42447
## 1796 84.0 0 0 0 23.50872
## 1797 84.0 1 1 1 33.01434
## 1798 79.0 0 0 0 24.90087
## 1799 75.0 0 0 0 20.50103
## 1800 84.0 1 0 0 23.88369
## 1801 84.0 0 0 0 27.03841
## 1802 84.0 0 0 0 23.93005
## 1803 77.0 0 0 0 23.40185
## 1804 68.5 0 0 0 20.95253
## 1805 95.0 0 0 0 29.82973
## 1806 87.5 1 0 0 25.03337
## 1807 84.0 1 0 0 23.95876
## 1808 86.0 1 0 0 21.57988
## 1809 68.0 1 0 0 18.01721
## 1810 87.0 0 0 0 22.29912
## 1811 100.5 1 0 0 29.14747
## 1812 84.0 0 0 0 20.36446
## 1813 77.0 1 0 0 25.13048
## 1814 104.0 1 0 0 29.95222
## 1815 75.9 1 0 0 21.22043
## 1816 75.5 0 0 0 21.56715
## 1817 105.0 0 0 0 33.37770
## 1818 90.0 1 1 1 26.37188
## 1819 86.0 0 0 0 24.49140
## 1820 84.0 0 0 0 18.38552
## 1821 84.0 0 0 0 23.87399
## 1822 66.0 0 0 0 21.33830
## 1823 75.0 0 0 0 20.30617
## 1824 90.0 0 0 0 27.92754
## 1825 69.0 0 0 0 21.85769
## 1826 102.0 0 0 0 36.87555
## 1827 84.0 0 0 0 30.29778
## 1828 78.0 1 1 1 25.61028
## 1829 83.0 0 0 0 24.73992
## 1830 89.0 0 0 0 26.33406
## 1831 84.0 0 1 1 24.76867
## 1832 90.0 1 0 0 25.73784
## 1833 102.0 0 0 0 25.67321
## 1834 84.0 0 0 0 20.19247
## 1835 84.0 0 1 1 24.37500
## 1836 96.0 0 0 0 25.07583
## 1837 102.0 0 1 1 30.26183
## 1838 66.0 1 0 0 19.89925
## 1839 94.0 1 0 0 30.62307
## 1840 69.0 0 0 0 19.08196
## 1841 100.9 1 0 0 34.08479
## 1842 88.5 0 0 0 23.80925
## 1843 84.0 1 0 0 25.76452
## 1844 61.0 0 1 1 16.08085
## 1845 80.0 1 0 0 24.96832
## 1846 84.0 0 0 0 34.21436
## 1847 98.0 0 0 0 27.47621
## 1848 84.0 1 1 1 22.76902
## 1849 90.5 0 1 1 29.63139
## 1850 59.0 0 0 0 18.85318
## 1851 60.0 0 0 0 17.65904
## 1852 85.0 0 1 1 30.10204
## 1853 93.0 1 1 1 25.02553
## 1854 94.0 1 1 1 26.26667
## 1855 89.0 1 0 0 23.84103
## 1856 85.0 0 1 1 24.29543
## 1857 84.0 0 0 0 30.93434
## 1858 77.0 0 0 0 23.25955
## 1859 99.0 1 0 0 30.29973
## 1860 91.0 1 1 1 26.77593
## 1861 80.0 1 0 0 22.82556
## 1862 85.3 1 0 0 25.28942
## 1863 65.0 0 0 0 19.31956
## 1864 90.5 0 0 0 25.15555
## 1865 84.0 0 0 0 22.83078
## 1866 80.0 0 0 0 23.11707
## 1867 102.0 0 0 0 36.20343
## 1868 101.0 1 0 0 36.20560
## 1869 84.0 1 0 0 33.42591
## 1870 71.0 1 0 0 20.40872
## 1871 84.0 0 0 0 24.25332
## 1872 94.5 1 0 0 29.38902
## 1873 69.0 0 0 0 21.12602
## 1874 79.0 1 1 1 26.25728
## 1875 68.0 0 0 0 19.55607
## 1876 84.0 1 0 0 30.85447
## 1877 80.5 0 0 0 22.82491
## 1878 72.0 0 1 1 20.98794
## 1879 87.0 1 0 0 23.33768
## 1880 86.0 0 0 0 24.05277
## 1881 84.0 0 0 0 26.05674
## 1882 71.0 0 0 0 25.53545
## 1883 85.5 0 1 1 22.15102
## 1884 93.0 0 0 0 26.27122
## 1885 96.5 1 0 0 35.88000
## 1886 84.0 0 0 0 28.09586
## 1887 84.5 0 0 0 28.25576
## 1888 98.5 1 0 0 23.55556
## 1889 91.0 0 0 0 30.84517
## 1890 86.5 0 0 0 25.76817
## 1891 84.0 0 0 0 31.52271
## 1892 90.0 1 0 0 25.07452
## 1893 65.5 1 0 0 19.00033
## 1894 87.5 1 1 1 31.71028
## 1895 76.5 0 1 1 20.67590
## 1896 86.5 0 1 1 25.67183
## 1897 67.5 0 0 0 19.05184
## 1898 87.5 0 0 0 27.69663
## 1899 96.0 1 0 0 24.69771
## 1900 72.0 0 1 1 20.70098
## 1901 67.0 0 0 0 19.70351
## 1902 81.0 1 0 0 26.34422
## 1903 62.5 0 0 0 17.08834
## 1904 92.0 0 0 0 28.85874
## 1905 84.0 0 0 0 24.78073
## 1906 84.0 1 0 0 26.02432
## 1907 88.5 1 0 0 24.78999
## 1908 71.5 1 0 0 19.67285
## 1909 81.0 1 0 0 24.57467
## 1910 97.0 0 1 1 29.93170
## 1911 63.0 1 0 0 18.65568
## 1912 77.5 0 0 0 24.38205
## 1913 67.0 1 0 0 18.27593
## 1914 81.0 1 0 0 22.07031
## 1915 102.8 0 0 0 27.80314
## 1916 83.0 0 0 0 26.80946
## 1917 84.0 1 0 0 18.06112
## 1918 98.0 1 0 0 31.97228
## 1919 81.0 0 0 0 25.54259
## 1920 78.0 1 0 0 23.56551
## 1921 68.0 1 0 0 21.31559
## 1922 88.0 0 0 0 25.80204
## 1923 85.0 0 0 0 25.24413
## 1924 73.0 0 0 0 17.96875
## 1925 79.5 0 0 0 20.37103
## 1926 84.0 0 0 0 19.25230
## 1927 58.0 1 0 0 19.73156
## 1928 73.5 0 1 1 20.21273
## 1929 92.0 1 0 0 30.49353
## 1930 66.0 0 0 0 21.36596
## 1931 77.0 1 0 0 21.57310
## 1932 62.5 0 1 1 20.35556
## 1933 92.0 1 0 0 26.70226
## 1934 73.0 1 1 1 21.55784
## 1935 89.5 0 0 0 25.75530
## 1936 84.0 0 1 1 26.48224
## 1937 78.0 0 0 0 25.24636
## 1938 86.5 0 0 0 30.33327
## 1939 80.0 1 0 0 24.29489
## 1940 61.0 0 0 0 18.30523
## 1941 73.5 0 0 0 23.71884
## 1942 84.0 0 0 0 20.95406
## 1943 86.0 1 0 0 26.49835
## 1944 92.6 0 0 0 26.46494
## 1945 82.3 0 0 0 25.30697
## 1946 85.5 1 0 0 27.68002
## 1947 90.0 1 1 1 30.20408
## 1948 84.0 1 0 0 21.48135
## 1949 98.5 0 0 0 29.55313
## 1950 71.0 1 0 0 20.35416
## 1951 78.0 1 0 0 27.10022
## 1952 84.0 0 0 0 29.01369
## 1953 81.0 0 0 0 25.65551
## 1954 90.0 0 0 0 28.16779
## 1955 97.5 1 0 0 31.78300
## 1956 88.0 1 1 1 30.72224
## 1957 76.0 0 0 0 20.91094
## 1958 74.0 1 1 1 21.37797
## 1959 89.0 1 1 1 23.63755
## 1960 84.0 0 0 0 21.96804
## 1961 88.0 1 0 0 25.96559
## 1962 84.0 1 0 0 26.17833
## 1963 79.0 0 0 0 20.85337
## 1964 85.3 1 1 1 26.81828
## 1965 106.0 1 0 0 30.52498
## 1966 84.5 0 0 0 22.76380
## 1967 85.0 0 0 0 25.07679
## 1968 78.5 0 0 0 20.96315
## 1969 84.0 1 0 0 27.25530
## 1970 76.5 1 0 0 21.39034
## 1971 100.0 0 0 0 42.27885
## 1972 81.0 0 0 0 24.49348
## 1973 87.0 0 0 0 26.79792
## 1974 86.0 0 0 0 24.82308
## 1975 61.5 1 0 0 20.67157
## 1976 68.0 0 0 0 20.15818
## 1977 68.5 0 0 0 21.75942
## 1978 84.0 0 0 0 26.90129
## 1979 80.0 1 0 0 23.77089
## 1980 78.5 1 0 0 25.28617
## 1981 95.0 1 0 0 28.70615
## 1982 80.0 1 0 0 26.10478
## 1983 80.0 1 0 0 27.02753
## 1984 80.0 0 0 0 24.52208
## 1985 91.3 0 0 0 25.83728
## 1986 82.0 1 0 0 24.53392
## 1987 84.0 0 0 0 22.35896
## 1988 71.5 1 0 0 19.69171
## 1989 77.0 1 0 0 27.63377
## 1990 107.5 0 0 0 39.25173
## 1991 67.0 1 0 0 21.91381
## 1992 94.5 0 0 0 33.08765
## 1993 84.0 1 0 0 24.03655
## 1994 93.0 0 0 0 27.84767
## 1995 68.0 0 0 0 20.75014
## 1996 70.8 0 0 0 22.02287
## 1997 79.0 1 0 0 22.58955
## 1998 76.0 1 0 0 23.77359
## 1999 83.0 0 1 1 23.73324
## 2000 90.0 0 0 0 27.34660
## 2001 72.5 0 0 0 23.20348
## 2002 92.0 1 0 0 28.35694
## 2003 97.5 0 0 0 33.80932
## 2004 86.2 1 0 0 27.39793
## 2005 61.0 1 0 0 17.86054
## 2006 81.0 1 0 0 30.29368
## 2007 96.5 0 0 0 28.16447
## 2008 78.3 1 0 0 25.42677
## 2009 98.5 0 0 0 28.38022
## 2010 114.0 1 0 0 32.33288
## 2011 82.0 0 0 0 25.81437
## 2012 87.0 1 0 0 23.96940
## 2013 90.0 1 0 0 29.30966
## 2014 76.0 0 0 0 22.18798
## 2015 84.0 1 0 0 25.67667
## 2016 95.0 0 0 0 30.67717
## 2017 88.0 0 0 0 23.50856
## 2018 94.0 1 0 0 28.02341
## 2019 88.0 1 0 0 28.10684
## 2020 88.0 1 0 0 30.08021
## 2021 82.0 1 0 0 23.27543
## 2022 84.0 0 0 0 19.09102
## 2023 92.0 0 0 0 32.11806
## 2024 84.0 1 0 0 30.80942
## 2025 72.0 1 0 0 20.27004
## 2026 87.0 1 0 0 28.62171
## 2027 91.0 0 0 0 23.76182
## 2028 90.0 0 0 0 35.10290
## 2029 84.0 1 0 0 16.14893
## 2030 97.0 1 0 0 25.19718
## 2031 105.0 1 0 0 32.52446
## 2032 80.0 0 0 0 22.54658
## 2033 93.0 1 0 0 27.32160
## 2034 79.0 1 0 0 22.55102
## 2035 84.0 0 0 0 24.17969
## 2036 69.0 0 0 0 20.05260
## 2037 91.0 0 0 0 22.89593
## 2038 75.0 0 0 0 22.04386
## 2039 87.0 1 0 0 24.01031
## 2040 95.0 1 0 0 24.53125
## 2041 106.0 0 0 0 33.75641
## 2042 93.0 0 0 0 31.17229
## 2043 91.5 0 0 0 27.05906
## 2044 78.0 0 0 0 24.46804
## 2045 77.0 1 0 0 23.15807
## 2046 84.0 1 0 0 22.91012
## 2047 84.0 0 0 0 28.87755
## 2048 149.2 1 0 0 37.55240
## 2049 79.0 1 0 0 22.33710
## 2050 84.0 0 0 0 23.55851
## 2051 84.0 0 0 0 23.23766
## 2052 84.0 0 0 0 19.99796
## 2053 86.5 1 1 1 27.63510
## 2054 89.0 0 0 0 29.59056
## 2055 84.0 0 1 1 23.79068
## 2056 84.0 0 0 0 28.47526
## 2057 84.0 0 0 0 21.89892
## 2058 93.0 0 1 1 32.48286
## 2059 102.0 1 0 0 27.50967
## 2060 71.0 1 0 0 20.68866
## 2061 95.0 1 1 1 30.89555
## 2062 81.0 1 0 0 31.09519
## 2063 99.0 1 0 0 31.65430
## 2064 84.0 1 0 0 28.13630
## 2065 92.5 0 1 1 23.82102
## 2066 72.5 0 0 0 22.55498
## 2067 86.0 0 0 0 22.87357
## 2068 69.5 0 0 0 20.58123
## 2069 99.0 0 0 0 33.07235
## 2070 88.0 1 0 0 27.36914
## 2071 67.6 0 0 0 20.81468
## 2072 82.0 1 0 0 33.33638
## 2073 84.0 1 0 0 25.45807
## 2074 84.0 1 0 0 30.11861
## 2075 72.0 0 0 0 20.38856
## 2076 69.0 0 0 0 20.68115
## 2077 78.0 1 0 0 23.80443
## 2078 78.0 0 0 0 22.56859
## 2079 71.0 0 0 0 18.93907
## 2080 102.0 0 0 0 33.64713
## 2081 84.0 1 0 0 25.67707
## 2082 70.5 1 0 0 22.37708
## 2083 89.0 1 0 0 28.02509
## 2084 94.0 0 0 0 29.49602
## 2085 71.3 0 0 0 22.70941
## 2086 84.0 1 0 0 22.78995
## 2087 84.0 0 0 0 24.93351
## 2088 81.0 1 0 0 22.52372
## 2089 84.0 0 0 0 26.66205
## 2090 85.0 0 0 0 27.23444
## 2091 99.0 1 0 0 32.34103
## 2092 67.0 1 1 1 19.90278
## 2093 74.0 0 0 0 19.91967
## 2094 67.5 0 0 0 16.80547
## 2095 74.0 1 0 0 23.26256
## 2096 84.0 1 0 0 19.91663
## 2097 91.0 1 0 0 25.93086
## 2098 74.0 0 0 0 24.90039
## 2099 84.0 1 0 0 20.27515
## 2100 68.5 0 0 0 21.09557
## 2101 86.0 0 0 0 27.57686
## 2102 92.0 1 0 0 27.34963
## 2103 93.5 0 0 0 26.75093
## 2104 84.0 1 0 0 17.48059
## 2105 75.0 1 0 0 21.63094
## 2106 84.0 0 0 0 23.76826
## 2107 80.0 0 0 0 19.42998
## 2108 88.0 0 0 0 25.78118
## 2109 82.0 0 1 1 21.20000
## 2110 93.0 0 0 0 33.27604
## 2111 98.0 1 0 0 25.00000
## 2112 98.0 0 0 0 28.40127
## 2113 74.0 1 0 0 21.30527
## 2114 104.0 0 1 1 30.12416
## 2115 84.0 1 1 1 22.71412
## 2116 84.0 0 0 0 20.71643
## 2117 86.0 1 0 0 25.04904
## 2118 107.0 1 0 0 34.20704
## 2119 128.0 0 0 0 44.63196
## 2120 87.0 1 0 0 24.33442
## 2121 92.0 0 0 0 33.06760
## 2122 95.0 0 0 0 28.81728
## 2123 86.0 0 0 0 30.01961
## 2124 97.0 1 0 0 22.23214
## 2125 81.0 0 0 0 24.28338
## 2126 84.0 1 0 0 26.67593
## 2127 90.0 1 0 0 25.36884
## 2128 75.0 0 0 0 16.95313
## 2129 85.0 0 0 0 27.59556
## 2130 103.0 1 0 0 30.61729
## 2131 84.0 1 0 0 22.70907
## 2132 84.0 0 0 0 25.53027
## 2133 74.0 0 0 0 18.30779
## 2134 84.0 1 0 0 27.77986
## 2135 78.0 0 0 0 25.04276
## 2136 84.0 1 0 0 22.80854
## 2137 84.0 1 0 0 25.43413
## 2138 89.0 1 0 0 27.63377
## 2139 75.0 1 0 0 25.08920
## 2140 84.0 1 0 0 27.05324
## 2141 104.0 0 0 0 36.74632
## 2142 85.0 0 0 0 30.70041
## 2143 83.0 1 0 0 21.27465
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## 3228 68.0 0 1 1 18.06127
## 3229 80.0 0 0 0 25.78512
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## 3259 84.0 1 0 0 18.36547
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## 3261 84.0 1 0 0 28.56999
## 3262 67.0 0 0 0 20.17801
## 3263 84.0 0 0 0 31.08305
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## 3277 84.0 0 0 0 22.66337
## 3278 100.0 0 0 0 32.32478
## 3279 80.0 0 0 0 24.94066
## 3280 84.0 0 0 0 19.23077
## 3281 93.0 1 0 0 24.51613
## 3282 77.0 0 0 0 21.07254
## 3283 85.0 0 0 0 26.11296
## 3284 73.0 1 1 1 22.37111
## 3285 84.0 0 0 0 27.96745
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## 3298 78.0 1 0 0 25.59063
## 3299 70.0 1 0 0 23.50994
## 3300 80.0 1 0 0 28.29809
## 3301 70.5 1 0 0 20.34828
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## 3303 95.0 0 0 0 27.21176
## 3304 89.0 1 0 0 27.01353
## 3305 99.0 1 0 0 29.32863
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## 3307 107.0 0 0 0 35.27123
## 3308 86.0 1 0 0 21.32959
## 3309 87.0 1 0 0 23.99737
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## 3312 87.0 0 0 0 27.65529
## 3313 69.0 0 0 0 20.23711
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## 3329 84.0 0 0 0 29.85724
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## 3430 101.0 1 0 0 30.02579
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## 3432 86.0 0 0 0 29.28010
## 3433 99.0 0 0 0 30.55507
## 3434 84.0 0 0 0 21.04683
## 3435 84.0 1 0 0 24.07369
## 3436 93.0 0 0 0 27.32861
## 3437 68.0 0 0 0 19.69356
## 3438 73.0 0 0 0 21.57393
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## 3440 84.0 0 0 0 29.51315
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## 3450 91.4 0 0 0 29.45813
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## 3587 82.0 0 0 0 26.17521
## 3588 89.5 1 0 0 26.26903
## 3589 81.0 1 0 0 23.53441
## 3590 100.0 0 0 0 31.48098
## 3591 77.5 1 0 0 22.52226
## 3592 84.0 0 0 0 26.01594
## 3593 83.5 0 0 0 28.04093
## 3594 101.0 1 0 0 31.51111
## 3595 84.0 0 0 0 25.00000
## 3596 104.0 1 0 0 32.61475
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## 3598 95.0 0 0 0 29.08622
## 3599 83.5 0 0 0 24.09801
## 3600 95.5 1 0 0 34.15416
## 3601 84.0 1 0 0 29.21953
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## 3603 84.0 1 0 0 21.94252
## 3604 106.0 0 0 0 38.22628
## 3605 94.5 0 0 0 27.02431
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## 3610 72.0 0 0 0 21.12573
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## 3612 83.0 0 0 0 24.40068
## 3613 84.0 0 0 0 25.74367
## 3614 78.0 0 0 0 21.58273
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## 3616 100.0 0 0 0 28.34467
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## 3620 73.0 1 0 0 20.22214
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## 3631 99.0 1 0 0 32.98969
## 3632 84.0 0 0 0 31.58687
## 3633 75.5 0 0 0 20.16529
## 3634 77.5 1 0 0 24.32852
## 3635 89.0 0 0 0 24.43322
## 3636 76.5 0 0 0 19.91137
## 3637 94.0 0 0 0 29.40188
## 3638 93.0 0 0 0 27.49677
## 3639 89.0 0 0 0 29.10465
## 3640 92.5 1 0 0 26.60257
## 3641 83.5 0 0 0 28.94511
## 3642 84.0 0 0 0 19.89929
## 3643 66.0 1 0 0 20.98735
## 3644 84.0 0 0 0 20.11887
## 3645 81.0 0 0 0 20.61754
## 3646 97.0 0 0 0 29.91501
## 3647 88.0 1 0 0 23.03564
## 3648 84.0 0 0 0 33.14009
## 3649 76.0 0 0 0 22.15683
## 3650 90.0 0 0 0 27.48194
## 3651 98.0 1 0 0 28.05690
## 3652 90.0 1 0 0 32.02902
## 3653 71.0 1 0 0 18.70549
## 3654 90.0 1 0 0 24.86008
## 3655 84.0 0 0 0 26.98137
## 3656 84.0 1 1 1 16.53948
## 3657 84.0 1 0 0 25.00000
## 3658 84.0 1 0 0 25.00000
## 3659 84.0 1 0 0 28.20052
## 3660 91.0 0 1 1 26.16766
## 3661 86.0 1 0 0 26.52557
## 3662 66.0 0 0 0 16.45312
## 3663 80.0 1 0 0 24.82045
## 3664 85.0 1 0 0 23.27503
## 3665 87.5 0 0 0 27.79940
## 3666 80.5 0 0 0 21.45809
## 3667 84.0 0 0 0 28.27015
## 3668 90.0 1 0 0 26.36483
## 3669 84.0 0 0 0 24.57353
## 3670 84.0 0 1 1 24.28694
## 3671 65.0 0 0 0 17.97222
## 3672 91.0 0 0 0 27.22840
## 3673 63.0 1 0 0 18.57778
## 3674 76.0 1 0 0 20.14768
## 3675 71.0 0 0 0 16.55661
## 3676 72.5 0 0 0 25.00000
## 3677 87.0 0 0 0 26.95680
## 3678 76.0 1 0 0 21.58917
## 3679 84.0 0 0 0 27.44094
## 3680 84.0 0 0 0 19.04732
## 3681 95.0 0 0 0 29.48065
## 3682 84.0 0 0 0 25.00000
## 3683 86.0 1 1 1 27.29692
## 3684 84.0 0 0 0 27.63504
## 3685 71.0 1 0 0 25.48093
## 3686 94.0 1 0 0 32.08780
## 3687 116.0 0 0 0 38.82807
## 3688 99.0 0 0 0 30.21198
## 3689 103.0 1 0 0 40.98388
## 3690 84.0 1 0 0 29.38654
## 3691 78.5 0 0 0 19.07873
## 3692 91.0 0 0 0 20.88940
## 3693 85.0 1 0 0 26.13333
## 3694 88.5 0 0 0 26.41914
## 3695 84.0 0 0 0 24.83288
## 3696 82.0 0 0 0 25.35651
## 3697 90.5 0 0 0 30.24859
## 3698 75.0 0 0 0 23.48804
## 3699 84.0 1 1 1 27.87396
## 3700 61.0 0 0 0 16.76052
## 3701 99.0 0 0 0 28.50576
## 3702 84.0 0 0 0 25.00000
## 3703 84.0 0 0 0 25.00000
## 3704 86.0 0 0 0 30.25450
## 3705 81.2 1 0 0 23.05047
## 3706 77.0 0 0 0 22.48889
## 3707 96.5 1 0 0 27.81921
## 3708 80.0 0 0 0 21.46205
## 3709 84.0 1 0 0 28.31197
## 3710 86.0 0 0 0 29.06169
## 3711 84.0 1 0 0 22.90047
## 3712 71.0 0 0 0 20.31250
## 3713 94.0 0 0 0 28.65417
## 3714 88.0 1 0 0 24.15167
## 3715 89.0 0 0 0 31.12507
## 3716 74.0 1 0 0 21.55559
## 3717 92.0 0 0 0 24.19649
## 3718 76.0 0 0 0 20.61574
## 3719 95.0 1 0 0 32.33162
## 3720 93.0 1 0 0 27.93422
## 3721 80.0 0 0 0 22.94447
## 3722 84.0 0 0 0 24.15846
## 3723 84.0 1 0 0 28.26887
## 3724 84.5 0 0 0 23.27267
## 3725 81.0 0 0 0 24.92497
## 3726 84.0 0 0 0 23.81235
## 3727 92.0 1 0 0 30.71241
## 3728 84.0 1 0 0 29.41616
## 3729 79.0 1 0 0 24.07347test_reglog## Target_PJK Age Sex GD_Puasa GD_PP Kolestrol LDL HDL Lipoprotein Merokok
## 1 0 49 1 87 55 239 165 30 88.0 4
## 2 0 32 1 84 85 174 105 46 70.8 4
## 3 0 60 1 104 114 206 117 36 78.7 0
## 4 0 36 2 100 151 165 101 44 88.0 3
## 5 0 32 2 95 111 251 182 50 80.0 0
## 6 0 33 2 82 102 150 94 49 82.0 0
## 7 0 32 2 93 96 148 79 43 88.0 4
## 8 0 55 1 144 146 118 64 45 67.0 3
## 9 0 41 2 98 116 160 103 43 75.0 0
## 10 1 43 2 98 116 160 103 43 87.5 3
## 11 0 33 2 77 115 181 103 21 85.0 0
## 12 0 26 2 119 120 194 135 35 116.0 3
## 13 1 34 2 83 129 168 103 52 71.0 0
## 14 0 27 2 86 87 196 128 48 60.3 3
## 15 0 60 1 97 88 175 119 45 98.0 0
## 16 1 54 1 87 103 260 164 69 66.0 3
## 17 0 64 2 84 107 194 128 45 79.0 4
## 18 0 34 1 80 90 213 147 28 84.0 3
## 19 1 55 2 89 125 182 116 56 96.0 0
## 20 0 52 2 86 120 286 215 54 100.3 4
## 21 0 52 2 86 134 244 150 46 92.0 4
## 22 1 42 2 86 130 190 116 62 90.0 4
## 23 0 59 1 75 128 283 189 56 77.0 0
## 24 0 27 2 86 103 184 98 71 76.0 4
## 25 1 60 1 81 97 121 78 36 72.0 0
## 26 0 53 2 90 83 255 146 83 63.0 3
## 27 0 34 2 81 167 192 122 55 88.0 4
## 28 1 46 2 94 114 193 123 53 88.0 4
## 29 1 39 1 93 82 224 172 40 81.5 0
## 30 0 35 2 84 156 221 160 45 90.0 0
## 31 0 25 2 74 54 144 76 44 83.0 3
## 32 0 35 1 67 68 195 125 46 69.0 3
## 33 1 29 2 72 126 169 106 39 83.0 4
## 34 0 26 2 81 101 162 80 64 60.0 0
## 35 0 40 2 93 141 221 148 50 76.2 4
## 36 0 36 2 234 401 278 194 35 107.0 4
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## 1511 0 38 2 181 380 232 175 45 93.0 0
## 1512 0 37 2 88 143 139 70 60 65.5 0
## 1513 0 41 2 85 141 183 123 56 77.5 0
## 1514 0 26 2 90 136 120 61 47 100.0 4
## 1515 0 52 2 89 135 192 120 59 97.0 0
## 1516 0 54 1 115 230 178 133 36 83.5 4
## 1517 0 26 2 81 72 176 103 62 77.0 0
## 1518 0 49 2 98 170 193 126 54 94.0 4
## 1519 0 40 2 90 124 193 120 62 80.7 0
## 1520 1 52 2 81 116 212 141 43 87.0 4
## 1521 0 32 2 100 194 202 124 52 96.0 4
## 1522 0 34 2 83 123 162 100 48 68.0 0
## 1523 0 40 2 88 107 146 95 44 83.0 0
## 1524 0 42 2 93 139 163 101 52 80.5 4
## 1525 0 38 2 94 103 186 111 60 81.0 0
## 1526 0 33 2 81 114 177 123 46 85.0 4
## 1527 0 40 1 83 104 161 96 45 67.5 0
## 1528 0 47 2 83 107 201 129 48 89.0 0
## 1529 0 42 1 84 99 193 124 53 77.0 0
## 1530 1 45 2 90 127 207 118 68 74.0 4
## 1531 0 27 2 85 118 203 129 50 80.0 0
## 1532 0 34 1 92 131 193 133 43 84.0 4
## 1533 0 32 2 101 137 174 111 63 84.5 3
## 1534 0 42 1 81 113 194 148 43 68.5 4
## 1535 0 39 2 90 111 165 87 68 72.5 4
## 1536 0 47 2 97 132 207 141 45 90.0 4
## 1537 0 32 2 90 127 159 93 56 75.5 4
## 1538 0 45 2 84 112 165 99 56 78.0 0
## 1539 0 32 2 89 134 163 108 49 84.5 4
## 1540 0 29 2 86 110 202 133 56 69.0 3
## 1541 0 42 2 88 117 190 128 39 89.5 0
## 1542 0 33 2 83 81 160 84 67 62.5 0
## 1543 0 46 2 86 130 211 157 43 83.0 4
## 1544 0 36 2 87 141 141 79 47 79.0 4
## 1545 0 47 2 93 147 245 155 60 79.5 0
## 1546 0 54 2 87 116 147 66 68 81.0 0
## 1547 0 54 2 172 341 284 222 62 91.0 0
## 1548 0 44 2 84 144 187 111 52 88.5 0
## 1549 0 49 1 93 121 192 104 42 80.2 4
## 1550 0 59 2 109 101 244 151 68 81.5 0
## 1551 0 35 2 328 497 150 95 49 113.0 3
## 1552 0 32 2 102 158 231 179 42 110.6 4
## 1553 0 26 2 87 79 131 76 49 74.0 3
## 1554 0 33 2 89 126 152 92 48 82.0 4
## 1555 0 49 2 93 112 230 139 82 80.6 4
## 1556 0 29 2 79 146 186 123 44 93.6 0
## 1557 0 33 2 88 115 200 129 56 83.5 0
## 1558 0 48 1 83 103 191 122 57 68.3 4
## 1559 0 33 2 91 118 158 95 44 95.2 4
## 1560 0 50 1 84 96 148 94 36 74.7 3
## 1561 0 33 2 84 100 193 142 46 77.0 4
## 1562 1 55 2 358 631 314 257 54 94.3 0
## 1563 0 26 2 85 118 203 129 50 80.0 0
## 1564 0 49 1 97 115 191 151 33 98.0 3
## 1565 0 53 2 90 110 239 144 71 82.9 4
## 1566 1 55 1 78 101 148 78 58 84.3 4
## 1567 0 27 2 88 108 147 90 43 109.1 4
## 1568 0 26 2 85 118 203 129 50 80.0 4
## 1569 0 25 2 77 113 166 99 49 112.8 3
## 1570 0 34 2 87 105 156 82 59 65.3 0
## 1571 0 53 2 102 215 233 168 38 98.5 3
## 1572 0 33 2 82 101 143 89 54 83.4 3
## 1573 0 28 2 80 92 167 98 59 81.5 3
## 1574 0 37 2 75 119 158 95 54 79.8 4
## 1575 0 36 2 100 132 182 118 49 109.9 4
## 1576 0 44 2 87 112 110 62 41 78.5 0
## 1577 0 40 2 85 118 203 129 50 80.0 0
## 1578 0 28 1 80 96 232 173 40 96.5 0
## 1579 0 29 2 78 113 174 107 66 73.0 0
## 1580 0 42 2 108 178 223 150 71 89.0 0
## 1581 0 42 2 111 222 198 139 37 88.5 0
## 1582 0 48 2 98 166 170 131 34 100.0 0
## 1583 0 48 2 231 272 172 77 53 93.0 0
## 1584 0 27 2 85 118 203 129 50 80.0 4
## 1585 0 30 2 88 100 175 120 51 83.0 0
## 1586 0 27 2 82 115 139 96 43 89.5 3
## 1587 0 31 1 85 117 193 138 41 84.0 4
## 1588 0 35 2 97 171 205 137 49 85.5 4
## 1589 0 31 2 89 96 188 106 69 89.6 0
## 1590 0 45 2 184 256 226 144 56 85.5 4
## 1591 0 25 1 87 80 214 170 42 79.5 0
## 1592 0 36 2 87 135 176 115 44 78.5 4
## 1593 0 32 1 101 103 185 130 31 100.0 3
## 1594 0 25 2 87 107 155 94 47 86.0 4
## 1595 0 44 2 97 140 154 100 53 76.0 0
## 1596 0 25 1 81 111 176 125 40 98.0 0
## 1597 0 37 2 89 121 174 101 64 76.7 4
## Lingkar_Perut Target_Hipertensi Nyeri_Dada Riwayat_Keluarga IMT
## 1 84.5 0 0 0 24.67105
## 2 78.5 1 1 1 22.18279
## 3 81.0 0 0 0 21.60410
## 4 79.0 1 0 0 23.24459
## 5 80.0 0 0 0 25.10388
## 6 84.0 1 1 1 24.13602
## 7 87.0 0 0 0 26.22222
## 8 69.0 1 0 0 19.38214
## 9 84.0 1 0 0 25.09242
## 10 86.0 1 1 1 25.10000
## 11 84.0 0 0 0 23.23893
## 12 118.0 1 0 0 38.87394
## 13 81.0 1 0 0 19.72387
## 14 84.0 0 0 0 19.35133
## 15 95.0 0 0 0 24.67550
## 16 84.0 0 1 1 19.38000
## 17 84.0 0 0 0 20.26936
## 18 80.8 1 0 0 26.61934
## 19 92.0 0 1 1 28.62147
## 20 101.0 0 0 0 29.96433
## 21 63.3 0 0 0 28.00000
## 22 88.0 0 0 0 26.29849
## 23 73.0 1 0 0 23.53304
## 24 85.5 0 0 0 20.54419
## 25 84.0 1 1 1 20.28480
## 26 56.0 0 0 0 17.12250
## 27 95.0 0 1 1 26.12495
## 28 89.0 1 0 0 24.83576
## 29 84.0 0 1 1 25.34435
## 30 93.0 1 0 0 32.19092
## 31 93.0 1 0 0 23.71884
## 32 74.0 1 0 0 19.72318
## 33 82.0 0 1 1 23.95449
## 34 63.0 1 0 0 17.11635
## 35 68.0 1 0 0 21.87872
## 36 101.0 1 0 0 35.37981
## 37 92.0 1 0 0 30.38502
## 38 84.0 0 1 1 30.12048
## 39 84.0 1 1 1 19.90997
## 40 84.0 0 1 1 24.14152
## 41 84.0 0 0 0 21.92886
## 42 84.0 1 0 0 20.04016
## 43 91.0 1 1 1 32.05128
## 44 93.0 1 0 0 25.27347
## 45 86.0 1 0 0 25.73333
## 46 84.0 1 1 1 30.11940
## 47 102.0 1 0 0 30.06210
## 48 84.0 1 0 0 25.71956
## 49 84.0 0 0 0 25.00000
## 50 84.0 0 0 0 30.11995
## 51 87.0 1 0 0 24.67028
## 52 91.0 1 0 0 30.75740
## 53 82.0 1 0 0 21.79608
## 54 84.0 0 0 0 26.54670
## 55 77.0 1 0 0 21.58003
## 56 78.0 0 0 0 20.39542
## 57 90.0 1 0 0 27.34375
## 58 73.0 0 1 1 24.46483
## 59 71.0 0 0 0 24.74725
## 60 84.0 1 0 0 23.79536
## 61 80.0 0 0 0 23.67926
## 62 85.0 1 0 0 24.97874
## 63 84.0 0 0 0 26.77517
## 64 65.0 0 0 0 16.79688
## 65 90.6 1 0 0 26.05835
## 66 84.0 0 1 1 21.49029
## 67 65.0 0 0 0 18.38235
## 68 79.0 0 0 0 22.43000
## 69 95.0 0 0 0 26.52392
## 70 84.0 1 0 0 24.36612
## 71 84.0 1 0 0 33.71000
## 72 60.0 0 0 0 18.71804
## 73 70.0 0 0 0 17.57707
## 74 96.0 0 1 1 25.60554
## 75 70.0 1 0 0 23.99446
## 76 76.0 0 0 0 24.88889
## 77 99.0 0 0 0 29.13632
## 78 92.0 0 0 0 31.55556
## 79 84.0 0 0 0 21.97232
## 80 84.0 0 0 0 21.62065
## 81 70.0 0 0 0 20.70082
## 82 84.0 0 0 0 21.02542
## 83 84.0 0 0 0 36.93527
## 84 70.5 1 0 0 20.44444
## 85 84.0 0 0 0 23.02000
## 86 73.0 0 0 0 23.24459
## 87 97.6 1 0 0 31.55556
## 88 84.0 1 0 0 27.90000
## 89 70.0 0 0 0 19.53938
## 90 64.0 0 1 1 23.73996
## 91 84.0 1 0 0 19.59646
## 92 78.0 0 1 1 26.66667
## 93 69.0 1 0 0 21.64412
## 94 80.0 0 0 0 28.14787
## 95 84.0 0 1 1 17.79993
## 96 78.0 0 0 0 27.11111
## 97 89.0 1 0 0 26.29849
## 98 86.0 1 0 0 25.31545
## 99 63.0 0 0 0 16.88019
## 100 92.0 0 1 1 29.41309
## 101 84.0 1 1 1 25.61000
## 102 84.0 1 0 0 23.54536
## 103 80.0 1 0 0 27.19179
## 104 74.0 1 0 0 21.58003
## 105 97.0 1 0 0 31.53011
## 106 77.0 0 1 1 22.50693
## 107 95.0 0 0 0 26.66667
## 108 92.0 0 0 0 26.76978
## 109 68.0 1 0 0 17.26354
## 110 80.0 1 0 0 26.91273
## 111 77.0 0 0 0 20.56933
## 112 74.0 0 0 0 20.89796
## 113 84.0 0 0 0 24.24392
## 114 84.0 0 1 1 24.03461
## 115 94.0 1 0 0 28.99931
## 116 83.0 0 0 0 23.37258
## 117 60.0 0 0 0 17.31341
## 118 78.0 1 0 0 22.49657
## 119 83.0 1 0 0 26.23358
## 120 71.0 0 0 0 22.36744
## 121 105.0 0 0 0 34.89229
## 122 84.0 0 0 0 22.03173
## 123 84.0 0 0 0 20.82920
## 124 80.0 1 0 0 22.33241
## 125 80.0 0 1 1 23.13875
## 126 88.0 0 1 1 28.38538
## 127 83.0 1 1 1 23.84468
## 128 84.0 1 1 1 25.33024
## 129 83.0 1 0 0 26.08303
## 130 85.0 1 0 0 24.91988
## 131 98.0 1 0 0 33.24445
## 132 68.0 0 0 0 24.43676
## 133 76.0 0 0 0 22.28752
## 134 64.0 1 0 0 16.63807
## 135 102.0 0 1 1 30.69232
## 136 100.0 0 1 1 34.65121
## 137 75.0 0 0 0 23.18030
## 138 73.0 1 1 1 20.73741
## 139 93.0 0 0 0 31.48735
## 140 49.0 0 0 0 20.13333
## 141 84.0 1 1 1 32.33416
## 142 80.0 0 0 0 25.64920
## 143 84.0 0 0 0 21.94425
## 144 92.0 0 0 0 28.37527
## 145 62.0 1 0 0 21.66572
## 146 84.0 1 0 0 19.26092
## 147 96.0 0 0 0 25.67697
## 148 91.0 0 0 0 29.41977
## 149 87.0 1 0 0 25.86423
## 150 84.0 1 0 0 26.52196
## 151 88.0 1 0 0 31.15336
## 152 70.0 1 0 0 26.03998
## 153 75.0 1 0 0 23.30572
## 154 87.0 1 0 0 24.80123
## 155 84.0 1 0 0 27.39443
## 156 60.0 1 0 0 17.53105
## 157 63.0 1 0 0 19.78356
## 158 91.0 0 0 0 29.58430
## 159 80.0 0 1 1 28.38604
## 160 74.0 0 0 0 16.93222
## 161 87.0 0 0 0 26.00823
## 162 84.0 0 0 0 25.92397
## 163 73.0 0 0 0 23.13173
## 164 84.0 0 0 0 17.50639
## 165 91.0 0 0 0 25.00000
## 166 80.0 0 0 0 26.01281
## 167 64.0 1 0 0 18.13748
## 168 81.0 0 0 0 23.37733
## 169 84.0 0 0 0 23.72179
## 170 84.0 0 0 0 17.99887
## 171 80.0 0 0 0 20.91721
## 172 84.0 1 0 0 18.88163
## 173 84.0 0 0 0 16.34958
## 174 82.0 0 0 0 27.39226
## 175 88.0 1 0 0 28.10532
## 176 64.0 1 0 0 18.41837
## 177 84.0 0 0 0 21.79784
## 178 73.0 0 0 0 22.23379
## 179 75.0 0 0 0 24.11874
## 180 84.0 1 0 0 20.32225
## 181 84.0 0 0 0 23.38876
## 182 79.0 0 0 0 23.70651
## 183 93.0 0 0 0 32.76517
## 184 80.0 0 1 1 21.97711
## 185 62.0 1 0 0 19.23057
## 186 84.0 0 0 0 34.13370
## 187 60.0 0 0 0 17.00882
## 188 83.0 0 0 0 23.52941
## 189 87.0 0 1 1 23.78121
## 190 72.0 0 0 0 19.39619
## 191 86.0 1 1 1 28.39335
## 192 106.0 1 1 1 40.78768
## 193 105.5 1 0 0 34.83871
## 194 60.0 0 0 0 17.66907
## 195 90.0 0 0 0 24.13891
## 196 84.0 1 0 0 15.89580
## 197 84.0 0 1 1 32.89492
## 198 104.0 0 0 0 34.80000
## 199 84.0 1 0 0 25.71166
## 200 84.0 0 0 0 21.67043
## 201 97.0 1 0 0 32.11000
## 202 78.0 1 0 0 22.87557
## 203 84.0 1 1 1 19.88000
## 204 98.0 0 0 0 27.67315
## 205 92.0 1 0 0 24.93032
## 206 77.0 0 0 0 25.00000
## 207 104.0 0 0 0 33.66087
## 208 95.0 1 0 0 24.92000
## 209 84.0 0 0 0 19.02137
## 210 70.0 0 0 0 18.89946
## 211 78.0 1 0 0 23.50948
## 212 79.0 1 0 0 25.83968
## 213 84.0 0 0 0 22.32469
## 214 83.0 0 0 0 26.00823
## 215 84.0 0 0 0 24.89856
## 216 82.0 0 0 0 24.07917
## 217 84.0 0 1 1 26.92546
## 218 84.0 0 0 0 23.87265
## 219 73.0 0 0 0 23.89185
## 220 87.0 0 0 0 31.24936
## 221 79.0 0 0 0 24.01080
## 222 65.0 0 0 0 19.68496
## 223 82.0 1 0 0 24.36889
## 224 84.0 0 0 0 31.67750
## 225 80.0 0 0 0 24.15325
## 226 75.0 1 1 1 20.50521
## 227 84.0 0 0 0 21.14632
## 228 80.0 0 0 0 24.00214
## 229 85.0 0 1 1 30.96415
## 230 88.0 0 0 0 31.08389
## 231 84.0 0 0 0 22.46838
## 232 84.0 0 0 0 25.00000
## 233 80.0 0 0 0 22.50614
## 234 83.0 0 0 0 23.52554
## 235 84.0 0 0 0 25.40000
## 236 73.0 0 0 0 24.94898
## 237 84.0 0 1 1 18.45903
## 238 86.0 0 1 1 28.45606
## 239 75.0 0 0 0 22.84285
## 240 73.0 0 0 0 23.53304
## 241 68.0 1 1 1 22.45351
## 242 82.0 0 1 1 21.73405
## 243 90.0 1 0 0 29.57289
## 244 80.0 0 0 0 27.86953
## 245 84.0 1 1 1 32.13319
## 246 93.0 1 1 1 27.59516
## 247 84.0 1 0 0 22.01071
## 248 79.0 0 0 0 26.79876
## 249 99.0 0 1 1 28.99149
## 250 84.0 1 1 1 23.62830
## 251 87.0 1 1 1 29.32491
## 252 80.0 1 1 1 21.65923
## 253 88.0 0 0 0 24.13969
## 254 88.0 0 0 0 28.77698
## 255 60.0 1 0 0 18.80774
## 256 63.0 1 0 0 20.64774
## 257 74.0 0 0 0 19.90047
## 258 64.0 0 0 0 18.33251
## 259 57.0 1 1 1 17.67466
## 260 89.0 0 0 0 25.82644
## 261 74.0 1 0 0 21.46667
## 262 80.0 0 0 0 26.27153
## 263 70.0 1 1 1 19.80592
## 264 73.0 0 0 0 19.25124
## 265 79.0 0 0 0 29.74742
## 266 73.0 0 0 0 24.70604
## 267 66.0 0 0 0 19.42747
## 268 67.0 0 0 0 23.04730
## 269 68.0 0 1 1 25.00000
## 270 86.0 1 0 0 27.10199
## 271 84.0 0 0 0 23.63403
## 272 84.0 1 0 0 26.77607
## 273 78.0 1 0 0 21.62869
## 274 70.0 0 0 0 20.35556
## 275 84.0 1 1 1 20.43614
## 276 77.0 0 1 1 21.72306
## 277 90.0 0 1 1 24.24916
## 278 83.0 0 0 0 23.23000
## 279 68.0 0 0 0 17.55342
## 280 77.0 1 0 0 22.64686
## 281 84.0 0 0 0 15.23413
## 282 74.0 0 0 0 21.04373
## 283 76.0 1 1 1 20.20420
## 284 72.0 0 1 1 19.33594
## 285 84.0 1 0 0 18.90510
## 286 72.0 1 0 0 22.83288
## 287 84.0 0 0 0 25.40608
## 288 84.0 0 0 0 19.40115
## 289 70.0 0 1 1 20.92325
## 290 84.0 0 0 0 20.52301
## 291 75.0 0 0 0 20.98673
## 292 70.0 1 0 0 17.63589
## 293 87.0 1 0 0 25.03092
## 294 80.0 1 0 0 22.41649
## 295 81.0 0 0 0 21.28583
## 296 76.0 1 0 0 23.01118
## 297 84.0 0 0 0 22.49662
## 298 88.0 0 0 0 25.35233
## 299 74.0 0 0 0 23.91672
## 300 84.0 0 0 0 20.95171
## 301 88.0 0 0 0 28.01387
## 302 80.0 0 0 0 21.88501
## 303 84.0 0 0 0 25.00000
## 304 75.0 0 0 0 25.46673
## 305 82.0 1 0 0 23.19049
## 306 84.0 1 0 0 27.18200
## 307 68.0 0 0 0 21.67208
## 308 75.0 1 1 1 17.93912
## 309 76.0 1 0 0 19.18816
## 310 75.0 0 1 1 30.62653
## 311 86.0 0 0 0 28.91137
## 312 88.5 1 0 0 25.41252
## 313 84.0 0 0 0 20.82466
## 314 91.0 1 0 0 28.96779
## 315 71.0 1 0 0 21.74766
## 316 68.0 0 0 0 19.68459
## 317 83.0 1 1 1 25.02553
## 318 74.0 1 0 0 19.65698
## 319 87.0 0 0 0 29.70067
## 320 86.0 1 0 0 19.75724
## 321 92.0 1 0 0 26.62453
## 322 84.0 1 0 0 22.57778
## 323 84.0 1 1 1 25.56905
## 324 72.0 0 1 1 23.56593
## 325 82.0 0 1 1 26.65950
## 326 69.0 1 1 1 20.67475
## 327 63.0 0 0 0 17.80475
## 328 91.0 0 0 0 35.46667
## 329 85.0 0 0 0 26.40712
## 330 89.0 0 0 0 27.30344
## 331 64.0 1 0 0 18.29689
## 332 70.0 1 0 0 19.77238
## 333 98.0 1 0 0 30.57172
## 334 64.0 1 0 0 21.10296
## 335 78.0 1 0 0 23.25207
## 336 78.0 1 0 0 21.80902
## 337 79.0 1 0 0 23.31851
## 338 97.0 0 0 0 31.15555
## 339 74.0 0 0 0 19.86902
## 340 82.0 0 0 0 24.75790
## 341 80.0 1 0 0 24.59349
## 342 83.0 1 0 0 21.83780
## 343 62.0 1 0 0 20.26647
## 344 73.0 1 1 1 20.70312
## 345 84.0 1 0 0 27.67874
## 346 79.0 1 0 0 25.96559
## 347 75.0 1 0 0 27.49013
## 348 70.0 0 0 0 24.13797
## 349 89.0 0 0 0 22.58673
## 350 73.0 1 0 0 23.71184
## 351 84.0 0 1 1 26.69989
## 352 84.0 0 0 0 17.09153
## 353 84.0 0 1 1 21.43347
## 354 73.0 0 0 0 20.89374
## 355 75.0 0 0 0 21.52695
## 356 84.0 1 0 0 27.09851
## 357 94.0 1 0 0 33.44905
## 358 84.0 1 1 1 23.39582
## 359 87.0 0 0 0 25.36952
## 360 84.0 1 0 0 26.53276
## 361 88.0 0 0 0 26.09938
## 362 84.0 0 0 0 22.86000
## 363 84.0 0 1 1 26.10107
## 364 68.0 0 1 1 23.39558
## 365 67.0 1 0 0 19.98878
## 366 87.0 1 0 0 30.71118
## 367 84.0 1 1 1 27.20811
## 368 73.0 1 0 0 23.01716
## 369 78.0 0 0 0 25.75312
## 370 80.0 1 0 0 23.43921
## 371 102.0 0 0 0 36.32746
## 372 75.5 1 0 0 21.82540
## 373 97.0 0 0 0 32.86224
## 374 74.0 1 0 0 21.74426
## 375 84.0 1 0 0 28.85631
## 376 90.0 1 1 1 25.97233
## 377 79.0 1 0 0 21.39224
## 378 100.0 0 0 0 32.04208
## 379 66.0 1 1 1 19.56463
## 380 112.5 0 1 1 37.18000
## 381 84.0 0 1 1 22.69896
## 382 75.0 0 1 1 23.67314
## 383 97.0 1 1 1 30.90234
## 384 90.0 1 0 0 30.93485
## 385 84.0 1 0 0 21.53469
## 386 84.0 0 1 1 23.12817
## 387 68.0 1 0 0 19.73439
## 388 76.0 1 0 0 20.67816
## 389 84.0 0 0 0 22.51943
## 390 90.0 0 0 0 26.36931
## 391 83.0 1 0 0 22.81457
## 392 100.0 1 0 0 32.53000
## 393 89.0 1 0 0 37.19500
## 394 74.0 1 0 0 26.05333
## 395 84.0 0 0 0 19.89498
## 396 84.0 1 0 0 28.71094
## 397 85.0 1 1 1 25.59834
## 398 90.0 0 0 0 29.97540
## 399 84.0 1 1 1 23.71640
## 400 92.5 0 0 0 31.23008
## 401 84.0 0 1 1 28.52201
## 402 76.0 1 0 0 28.54044
## 403 73.5 0 0 0 19.45858
## 404 65.0 0 1 1 20.31600
## 405 96.0 0 0 0 32.96078
## 406 84.0 1 1 1 25.07611
## 407 80.0 0 1 1 28.80465
## 408 84.0 0 0 0 33.78906
## 409 84.0 0 0 0 27.55351
## 410 81.0 1 0 0 31.95377
## 411 95.0 0 0 0 27.58613
## 412 84.0 0 0 0 27.33278
## 413 90.0 0 0 0 33.31431
## 414 84.0 0 0 0 22.01873
## 415 84.0 0 1 1 32.83636
## 416 84.0 0 0 0 19.46740
## 417 84.0 1 0 0 23.75882
## 418 73.0 0 0 0 26.17455
## 419 84.0 0 0 0 18.86234
## 420 67.0 1 0 0 25.00000
## 421 84.0 0 1 1 18.83633
## 422 69.0 1 0 0 19.02714
## 423 84.0 0 0 0 22.30204
## 424 92.5 0 1 1 27.04964
## 425 62.0 0 0 0 20.30463
## 426 98.5 0 0 0 36.77988
## 427 84.0 0 0 0 18.98560
## 428 66.5 0 1 1 20.22431
## 429 75.0 0 0 0 22.49135
## 430 81.5 0 0 0 24.68490
## 431 74.5 1 1 1 20.89455
## 432 95.0 1 1 1 29.31630
## 433 73.5 0 0 0 19.40808
## 434 84.0 0 0 0 19.47894
## 435 86.0 0 0 0 26.10107
## 436 84.0 1 1 1 26.38787
## 437 84.0 1 0 0 22.39710
## 438 84.0 0 1 1 22.53123
## 439 69.0 1 0 0 19.51963
## 440 76.0 1 0 0 21.47254
## 441 84.0 0 0 0 21.76000
## 442 69.0 0 0 0 18.38648
## 443 65.0 1 0 0 15.57576
## 444 84.0 0 0 0 26.55387
## 445 87.0 0 0 0 26.04182
## 446 100.0 0 1 1 31.44090
## 447 85.0 1 1 1 19.58937
## 448 84.0 0 0 0 18.92894
## 449 94.0 0 0 0 29.16437
## 450 93.0 1 0 0 28.91291
## 451 104.0 0 1 1 37.89200
## 452 75.0 0 0 0 20.51749
## 453 69.0 1 0 0 24.97647
## 454 72.0 0 0 0 19.56235
## 455 66.0 0 0 0 21.40469
## 456 86.0 0 0 0 25.05997
## 457 84.0 1 0 0 20.65960
## 458 84.0 1 1 1 19.20475
## 459 104.0 0 0 0 34.03589
## 460 84.0 0 0 0 22.17993
## 461 80.0 0 0 0 24.24905
## 462 96.0 0 0 0 29.72147
## 463 84.0 0 0 0 14.97384
## 464 69.0 0 0 0 19.51638
## 465 88.0 0 0 0 23.75179
## 466 84.0 0 0 0 19.51841
## 467 84.0 0 0 0 29.82164
## 468 87.0 1 0 0 27.11002
## 469 79.0 0 0 0 22.62609
## 470 84.0 0 0 0 23.33052
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## 1568 84.0 1 0 0 25.00000
## 1569 101.0 1 0 0 32.94582
## 1570 84.0 1 0 0 17.90050
## 1571 92.5 0 0 0 32.91636
## 1572 79.0 1 0 0 25.43499
## 1573 80.0 1 0 0 28.34455
## 1574 78.0 0 0 0 23.59751
## 1575 96.0 1 0 0 35.49553
## 1576 80.0 0 0 0 24.47472
## 1577 84.0 1 0 0 25.00000
## 1578 84.0 1 0 0 29.76009
## 1579 71.0 0 0 0 17.86423
## 1580 84.0 0 0 0 27.55102
## 1581 90.0 0 1 1 31.87144
## 1582 100.0 1 0 0 30.63501
## 1583 94.0 0 0 0 28.79892
## 1584 84.0 1 0 0 25.00000
## 1585 79.0 0 0 0 26.42630
## 1586 88.0 0 0 0 30.44619
## 1587 89.0 0 0 0 23.18612
## 1588 91.5 0 0 0 28.98293
## 1589 94.5 1 0 0 32.64422
## 1590 80.0 0 0 0 26.17778
## 1591 77.5 0 0 0 21.73333
## 1592 80.0 1 0 0 24.00000
## 1593 96.0 1 0 0 31.72199
## 1594 91.0 0 0 0 25.34188
## 1595 69.5 0 0 0 20.77944
## 1596 94.5 0 0 0 28.21225
## 1597 77.0 0 0 0 21.38516table(train_reglog$Target_PJK)##
## 0 1
## 3398 331table(test_reglog$Target_PJK)##
## 0 1
## 1456 141library(UBL)## Warning: package 'UBL' was built under R version 4.2.3## Loading required package: MBA## Warning: package 'MBA' was built under R version 4.2.3## Loading required package: gstat## Warning: package 'gstat' was built under R version 4.2.3## Loading required package: automap## Warning: package 'automap' was built under R version 4.2.3## Loading required package: sp## Warning: package 'sp' was built under R version 4.2.3## Loading required package: randomForest## Warning: package 'randomForest' was built under R version 4.2.3## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combineset.seed(123)
smote_reglog <- SmoteClassif(form = Target_PJK ~ ., dat = train_reglog, C.perc = list("1"=1500/331,"0"=1500/3398), dist = "HVDM")
prop_table <- prop.table(table(smote_reglog$Target_PJK))
print(prop_table)##
## 0 1
## 0.5 0.5table(smote_reglog$Target_PJK)##
## 0 1
## 1500 1500library(ROSE)## Warning: package 'ROSE' was built under R version 4.2.3## Loaded ROSE 0.0-4oversampling_reglog <- ROSE(Target_PJK ~ ., data = train_reglog, seed = 123)$data
# Compute class proportions
prop_table <- prop.table(table(oversampling_reglog$Target_PJK))
print(prop_table)##
## 0 1
## 0.5028158 0.4971842table(oversampling_reglog$Target_PJK)##
## 0 1
## 1875 1854undersampled_reglog <- ovun.sample(Target_PJK ~ ., data = train_reglog, method = "under")$data
# Melihat data yang telah diundersampling
print(undersampled_reglog)## Target_PJK Age Sex GD_Puasa GD_PP Kolestrol LDL HDL Lipoprotein Merokok
## 1 0 55 2 91 84 194 105 65 68.0 4
## 2 0 25 2 82 158 185 131 44 70.0 4
## 3 0 44 1 88 76 210 147 48 60.1 0
## 4 0 49 2 105 111 179 84 77 64.0 4
## 5 0 40 2 95 158 170 94 56 90.5 0
## 6 0 45 2 88 131 177 94 66 68.0 0
## 7 0 56 2 82 200 239 162 56 85.0 0
## 8 0 30 2 89 89 161 94 56 82.0 4
## 9 0 63 2 91 207 209 147 46 77.0 4
## 10 0 27 2 84 133 192 113 58 84.0 0
## 11 0 27 2 78 143 194 137 38 90.0 4
## 12 0 35 1 99 229 206 137 36 96.0 0
## 13 0 47 2 83 130 281 208 50 80.8 4
## 14 0 32 2 76 195 216 131 51 93.5 0
## 15 0 39 2 80 121 189 129 42 80.8 0
## 16 0 27 2 83 131 215 158 50 94.0 0
## 17 0 41 1 80 119 189 116 60 86.0 4
## 18 0 60 1 81 153 244 148 44 90.0 3
## 19 0 41 2 75 119 195 121 55 75.0 4
## 20 0 57 2 82 100 259 146 69 101.0 0
## 21 0 28 1 80 104 168 122 35 98.0 0
## 22 0 63 1 83 105 185 127 34 89.5 4
## 23 0 43 1 81 100 204 129 56 80.7 0
## 24 0 35 2 77 138 267 165 89 80.0 0
## 25 0 33 1 86 70 187 124 48 77.5 0
## 26 0 26 2 100 100 180 113 53 91.2 4
## 27 0 47 2 75 96 193 131 52 80.5 4
## 28 0 45 2 96 112 238 155 58 104.0 0
## 29 0 39 2 86 147 197 131 49 87.0 4
## 30 0 25 1 88 63 151 103 45 60.3 3
## 31 0 48 1 85 94 144 93 33 67.0 0
## 32 0 32 2 81 176 196 126 54 82.0 4
## 33 0 50 1 76 103 179 117 36 67.0 3
## 34 0 62 1 66 96 178 126 38 60.9 4
## 35 0 39 2 91 122 189 126 48 74.0 0
## 36 0 27 2 66 86 128 72 48 80.0 0
## 37 0 29 1 83 91 234 162 53 65.0 0
## 38 0 28 2 84 120 253 158 53 92.0 4
## 39 0 50 2 94 148 281 171 60 87.0 4
## 40 0 59 1 93 236 200 137 33 86.0 0
## 41 0 48 2 97 135 202 142 41 93.0 0
## 42 0 45 1 80 81 210 143 40 68.0 4
## 43 0 54 2 89 111 259 174 52 81.0 3
## 44 0 40 2 112 151 143 86 46 77.5 4
## 45 0 44 2 73 152 174 107 44 103.0 4
## 46 0 46 1 86 86 207 139 50 91.5 3
## 47 0 57 2 90 162 212 131 63 70.2 4
## 48 0 61 1 108 137 178 105 53 86.0 3
## 49 0 33 2 82 158 229 144 42 109.0 0
## 50 0 49 1 82 89 183 140 32 90.7 0
## 51 0 46 2 105 175 228 168 43 85.0 4
## 52 0 51 1 87 111 185 132 36 79.5 4
## 53 0 26 1 88 91 252 168 60 110.5 0
## 54 0 38 1 87 94 205 153 44 72.0 3
## 55 0 30 1 78 111 191 135 44 70.2 4
## 56 0 40 2 83 61 163 113 46 97.0 4
## 57 0 34 2 90 129 170 78 69 63.0 0
## 58 0 45 2 74 91 193 90 65 68.0 0
## 59 0 50 2 89 112 223 138 55 96.5 3
## 60 0 59 2 109 257 193 116 53 83.0 0
## 61 0 43 2 85 125 203 116 65 70.0 3
## 62 0 26 1 82 81 199 134 36 80.1 4
## 63 0 46 2 84 148 177 111 55 90.0 0
## 64 0 49 2 92 106 195 110 73 81.0 3
## 65 0 39 1 80 80 239 177 43 92.0 4
## 66 0 55 2 77 100 252 146 76 93.0 0
## 67 0 43 2 93 121 217 152 45 72.1 0
## 68 0 32 1 96 117 253 137 38 80.9 4
## 69 0 44 1 70 111 299 202 60 71.0 3
## 70 0 31 1 78 133 148 104 39 70.7 4
## 71 0 32 1 91 122 229 149 50 70.9 4
## 72 0 54 2 98 149 192 126 48 82.7 0
## 73 0 47 1 114 198 176 138 36 104.6 4
## 74 0 39 2 84 139 260 155 48 82.5 3
## 75 0 47 2 74 80 184 108 55 69.0 0
## 76 0 40 2 113 232 249 164 38 82.0 4
## 77 0 46 2 77 127 182 107 54 67.0 0
## 78 0 40 2 82 119 223 157 48 77.0 3
## 79 0 57 1 77 110 203 132 48 68.0 3
## 80 0 50 1 104 97 191 113 61 83.0 3
## 81 0 44 2 73 122 229 163 47 75.0 3
## 82 0 43 2 82 108 237 135 43 80.0 0
## 83 0 60 1 90 90 207 135 35 95.0 0
## 84 0 41 2 77 87 198 129 48 90.0 0
## 85 0 60 2 102 110 176 104 55 93.0 4
## 86 0 59 1 89 97 263 186 64 90.5 4
## 87 0 49 1 80 116 179 111 36 77.0 0
## 88 0 47 2 92 91 233 155 69 60.4 3
## 89 0 33 2 72 114 168 103 51 74.0 4
## 90 0 41 1 97 91 178 119 43 60.7 3
## 91 0 49 2 96 121 191 138 41 100.3 4
## 92 0 59 1 89 185 189 126 43 68.0 0
## 93 0 61 1 147 104 207 138 40 79.0 0
## 94 0 54 1 78 84 242 157 53 66.0 0
## 95 0 33 2 82 130 186 143 31 71.0 0
## 96 0 56 2 87 148 268 146 98 88.0 3
## 97 0 38 1 92 59 190 134 47 60.3 0
## 98 0 50 1 78 86 215 142 36 67.5 4
## 99 0 56 2 99 106 197 117 76 82.0 4
## 100 0 55 1 83 137 182 123 52 80.9 0
## 101 0 57 1 79 148 176 83 60 62.0 3
## 102 0 27 1 85 108 143 100 37 60.6 3
## 103 0 30 1 64 81 229 154 38 70.9 4
## 104 0 46 2 85 134 169 92 51 69.0 0
## 105 0 49 2 79 101 185 123 49 93.0 4
## 106 0 62 1 100 155 180 108 42 81.0 0
## 107 0 33 1 89 69 140 91 43 73.0 0
## 108 0 47 2 83 120 191 118 60 64.0 3
## 109 0 43 1 92 122 157 58 34 80.2 0
## 110 0 45 2 73 88 203 141 60 90.0 0
## 111 0 56 2 93 153 255 171 44 116.0 4
## 112 0 29 2 78 82 185 131 49 83.0 0
## 113 0 44 1 80 140 186 110 42 79.0 3
## 114 0 34 2 75 124 158 105 45 85.0 3
## 115 0 53 2 77 90 273 184 65 94.0 0
## 116 0 40 2 78 113 170 103 44 93.0 4
## 117 0 56 1 85 101 214 137 55 66.4 0
## 118 0 38 2 84 109 222 130 67 76.0 4
## 119 0 35 2 79 120 150 88 52 94.5 3
## 120 0 48 2 77 72 187 108 42 95.0 4
## 121 0 50 2 86 200 209 126 54 89.0 3
## 122 0 60 2 80 94 165 90 53 80.0 4
## 123 0 41 1 89 154 202 135 41 80.2 0
## 124 0 34 1 87 127 212 142 50 70.4 3
## 125 0 43 2 95 142 210 122 69 69.0 0
## 126 0 25 1 87 104 231 155 46 60.6 4
## 127 0 34 2 107 151 185 79 41 90.2 4
## 128 0 41 2 92 144 189 120 50 77.0 3
## 129 0 58 1 99 173 243 162 37 88.0 4
## 130 0 30 2 71 88 148 97 38 80.0 3
## 131 0 40 1 81 104 241 161 45 80.0 0
## 132 0 32 1 76 145 189 115 54 71.5 3
## 133 0 50 2 188 249 200 138 54 100.0 0
## 134 0 46 2 96 108 171 113 53 70.9 4
## 135 0 37 2 89 113 177 111 49 84.0 0
## 136 0 56 1 107 309 162 116 46 90.8 0
## 137 0 55 1 102 117 273 183 63 82.5 0
## 138 0 27 2 73 103 204 108 74 98.0 3
## 139 0 40 2 84 155 197 129 59 98.0 3
## 140 0 55 2 80 161 286 182 62 87.0 3
## 141 0 54 2 91 126 196 132 42 89.0 4
## 142 0 61 2 86 136 264 195 56 83.5 0
## 143 0 53 2 94 158 207 77 58 97.0 0
## 144 0 54 2 87 146 295 200 68 77.5 4
## 145 0 58 1 94 246 300 206 46 83.0 4
## 146 0 53 2 82 87 212 124 54 82.0 3
## 147 0 53 2 83 104 155 82 60 60.9 4
## 148 0 52 1 81 119 213 101 37 95.0 0
## 149 0 46 2 84 141 335 232 49 87.5 0
## 150 0 44 2 87 147 228 157 39 79.0 0
## 151 0 43 2 89 99 155 88 57 86.0 0
## 152 0 59 1 235 477 281 170 50 90.0 0
## 153 0 34 1 82 96 217 117 48 72.0 0
## 154 0 32 2 71 112 172 104 56 82.0 3
## 155 0 22 2 69 78 101 22 69 60.5 4
## 156 0 42 1 74 114 158 83 46 67.0 4
## 157 0 40 2 90 143 147 88 50 73.0 3
## 158 0 30 2 89 122 161 95 52 80.0 0
## 159 0 59 1 388 445 252 170 43 73.0 0
## 160 0 61 1 96 158 115 49 24 88.0 0
## 161 0 40 1 86 124 273 220 45 83.0 4
## 162 0 29 2 69 124 196 129 54 89.5 0
## 163 0 39 2 81 128 239 182 48 101.0 3
## 164 0 28 2 83 117 234 182 44 82.0 0
## 165 0 46 2 87 100 195 134 60 70.0 3
## 166 0 52 2 85 100 223 164 55 72.5 3
## 167 0 39 2 76 134 156 96 49 79.0 4
## 168 0 43 2 73 106 179 101 54 87.0 4
## 169 0 30 2 85 92 176 90 60 70.0 4
## 170 0 62 2 91 137 169 97 54 89.0 4
## 171 0 39 2 89 135 238 155 53 90.0 4
## 172 0 30 2 78 161 194 130 40 73.0 3
## 173 0 36 2 102 143 275 198 65 80.3 4
## 174 0 30 1 89 83 193 124 54 60.1 4
## 175 0 46 2 81 108 161 91 52 57.0 0
## 176 0 31 2 81 114 173 88 70 70.7 4
## 177 0 56 1 83 99 212 162 46 90.5 3
## 178 0 28 2 87 84 160 114 45 70.9 4
## 179 0 30 2 85 118 203 129 50 80.0 4
## 180 0 38 1 81 100 203 144 58 90.2 3
## 181 0 52 2 98 134 234 126 81 60.0 0
## 182 0 44 1 86 106 194 130 38 70.8 4
## 183 0 62 1 74 167 212 137 55 67.0 0
## 184 0 44 2 76 163 278 210 43 87.0 0
## 185 0 29 2 68 126 135 81 45 91.0 4
## 186 0 46 2 82 140 223 156 44 87.0 4
## 187 0 38 1 96 105 183 54 32 80.6 4
## 188 0 31 2 86 118 182 115 46 80.8 4
## 189 0 29 2 78 135 147 87 53 74.0 4
## 190 0 50 2 151 355 228 175 51 88.7 4
## 191 0 54 2 99 153 265 168 65 74.0 3
## 192 0 52 1 80 154 218 155 39 80.8 4
## 193 0 58 1 83 111 177 110 48 60.3 3
## 194 0 58 1 57 140 222 169 37 96.0 4
## 195 0 31 2 97 135 222 141 62 60.2 0
## 196 0 54 2 71 95 187 109 51 75.0 0
## 197 0 35 2 79 81 218 140 61 74.0 4
## 198 0 34 2 89 132 172 100 51 78.0 0
## 199 0 51 2 81 134 214 124 68 93.0 0
## 200 0 26 2 83 154 237 160 59 100.0 4
## 201 0 62 2 92 110 201 132 47 70.0 0
## 202 0 40 2 76 146 181 124 42 66.0 0
## 203 0 51 2 79 89 262 164 58 70.1 3
## 204 0 45 1 88 115 221 134 40 67.0 4
## 205 0 61 2 88 111 186 105 56 86.0 4
## 206 0 53 2 88 146 275 177 54 93.0 3
## 207 0 59 2 83 198 235 151 70 70.4 4
## 208 0 27 2 86 183 201 132 42 88.0 3
## 209 0 54 1 72 141 185 140 38 73.0 0
## 210 0 62 2 98 159 227 168 45 85.0 4
## 211 0 52 2 82 142 217 139 51 90.0 3
## 212 0 52 2 72 127 243 187 48 74.0 0
## 213 0 52 2 87 132 219 119 65 85.0 3
## 214 0 42 2 87 112 207 150 44 90.0 4
## 215 0 46 2 79 65 238 152 67 80.4 4
## 216 0 41 2 87 192 236 142 41 90.0 3
## 217 0 42 1 94 95 138 81 50 60.5 0
## 218 0 44 2 81 113 192 117 57 70.5 4
## 219 0 29 2 87 128 199 128 53 85.5 0
## 220 0 34 2 82 133 157 93 46 65.0 4
## 221 0 38 2 96 180 202 76 37 93.5 4
## 222 0 30 1 79 91 115 69 37 60.6 4
## 223 0 49 1 85 107 171 110 50 77.0 0
## 224 0 35 2 78 108 204 86 87 79.0 0
## 225 0 28 1 92 203 160 83 36 93.0 4
## 226 0 58 1 84 130 212 147 49 70.3 3
## 227 0 50 2 108 261 175 93 64 70.8 3
## 228 0 48 2 74 108 173 102 53 107.0 3
## 229 0 52 2 94 134 220 128 71 80.0 4
## 230 0 64 1 91 123 151 89 48 98.2 0
## 231 0 43 2 91 149 131 86 43 95.0 0
## 232 0 41 2 87 103 157 96 52 80.0 3
## 233 0 26 2 83 116 183 113 51 78.5 0
## 234 0 48 1 79 77 186 126 48 70.9 3
## 235 0 46 2 94 122 240 167 56 83.0 4
## 236 0 31 2 84 111 140 75 50 74.0 4
## 237 0 26 2 75 82 179 104 51 78.0 4
## 238 0 61 2 83 98 195 103 62 77.0 3
## 239 0 42 1 79 97 234 188 37 65.0 0
## 240 0 44 1 85 124 200 127 64 60.7 0
## 241 0 32 2 86 100 144 89 36 70.9 4
## 242 0 41 2 81 117 216 146 50 103.0 4
## 243 0 53 1 94 185 207 127 54 77.0 4
## 244 0 44 1 87 151 216 125 32 96.0 0
## 245 0 49 2 75 113 221 150 57 70.5 3
## 246 0 44 2 96 142 194 112 48 84.5 4
## 247 0 42 1 94 113 181 121 39 75.5 4
## 248 0 50 1 88 89 180 113 38 90.4 0
## 249 0 25 2 73 101 162 96 53 79.0 0
## 250 0 47 2 96 133 215 144 66 92.5 0
## 251 0 42 2 85 107 200 120 64 82.0 4
## 252 0 33 1 88 118 203 158 32 90.7 0
## 253 0 33 2 71 109 235 159 74 70.0 0
## 254 0 60 1 95 171 177 124 33 102.0 0
## 255 0 64 1 84 133 240 155 71 71.5 4
## 256 0 60 1 90 123 161 97 50 74.0 0
## 257 0 24 2 80 92 157 92 62 70.2 4
## 258 0 35 1 73 120 172 106 39 70.9 4
## 259 0 37 2 84 127 196 127 52 76.0 0
## 260 0 44 2 86 115 196 128 58 70.7 4
## 261 0 37 2 66 97 177 106 59 85.0 3
## 262 0 47 1 84 139 224 151 55 69.5 4
## 263 0 51 1 71 111 190 122 50 70.2 0
## 264 0 56 2 98 150 196 118 66 80.6 0
## 265 0 39 2 70 251 166 80 61 63.0 0
## 266 0 34 1 85 98 191 137 47 75.0 4
## 267 0 29 2 68 86 149 76 65 70.5 0
## 268 0 46 2 79 148 265 175 64 90.2 0
## 269 0 31 2 83 116 165 101 52 99.0 0
## 270 0 37 1 92 116 236 159 38 82.0 4
## 271 0 54 2 292 365 244 175 55 83.0 0
## 272 0 30 2 81 100 187 106 60 72.5 4
## 273 0 48 1 104 207 236 153 38 100.5 3
## 274 0 35 2 67 106 207 148 46 89.1 3
## 275 0 45 2 136 254 236 146 52 80.0 0
## 276 0 52 1 83 110 195 141 51 90.1 3
## 277 0 53 1 101 138 137 120 54 80.7 4
## 278 0 34 2 71 131 151 85 44 81.5 0
## 279 0 37 2 87 157 140 74 56 60.6 0
## 280 0 40 2 86 99 241 163 68 70.0 3
## 281 0 53 1 79 147 149 90 43 70.7 3
## 282 0 52 1 92 93 181 101 51 66.0 4
## 283 0 42 2 80 132 186 127 59 62.0 0
## 284 0 35 2 94 158 234 118 44 95.0 0
## 285 0 38 2 93 170 251 151 51 92.0 3
## 286 0 39 2 83 119 152 95 38 78.0 3
## 287 0 28 2 83 110 188 116 60 92.1 4
## 288 0 61 1 88 118 187 126 54 82.0 0
## 289 0 35 2 86 130 175 113 40 78.5 4
## 290 0 42 2 77 127 172 116 47 70.7 0
## 291 0 30 1 80 82 191 112 42 90.2 4
## 292 0 44 2 87 112 263 171 71 102.5 4
## 293 0 39 2 91 138 189 107 74 64.0 3
## 294 0 52 2 107 143 221 148 50 98.0 0
## 295 0 57 1 81 105 262 166 36 85.6 4
## 296 0 43 2 82 138 259 178 50 83.0 0
## 297 0 48 2 190 335 253 109 41 87.0 3
## 298 0 55 2 86 162 233 171 50 100.0 4
## 299 0 32 2 88 131 213 139 56 82.2 3
## 300 0 25 2 76 105 204 139 60 72.0 0
## 301 0 55 2 89 171 201 130 51 96.0 4
## 302 0 36 1 87 80 202 145 35 70.4 0
## 303 0 45 2 94 188 231 137 50 94.0 3
## 304 0 35 2 71 142 228 174 39 93.0 0
## 305 0 29 2 77 134 189 121 47 69.5 0
## 306 0 57 2 92 110 234 164 41 63.5 3
## 307 0 41 2 93 126 174 105 53 80.4 4
## 308 0 36 2 72 96 152 89 44 66.0 0
## 309 0 63 2 95 151 227 90 38 91.0 0
## 310 0 30 2 80 92 166 89 46 68.0 4
## 311 0 57 1 108 207 254 189 43 76.0 3
## 312 0 50 2 101 183 227 159 42 91.0 0
## 313 0 57 1 90 157 236 168 66 80.8 4
## 314 0 32 2 77 129 240 173 46 85.0 0
## 315 0 63 2 89 99 158 92 58 60.2 3
## 316 0 35 2 83 105 189 128 43 102.0 0
## 317 0 37 1 86 120 183 116 43 80.3 0
## 318 0 65 2 85 79 187 100 75 83.0 4
## 319 0 27 2 81 103 167 104 36 74.0 0
## 320 0 55 1 71 81 207 111 64 99.0 0
## 321 0 62 2 95 200 330 231 39 92.0 0
## 322 0 57 2 78 128 260 187 46 80.0 0
## 323 0 58 2 316 484 146 61 58 76.0 0
## 324 0 27 2 84 118 209 148 47 84.0 3
## 325 0 62 2 81 92 262 185 48 70.7 4
## 326 0 53 2 107 153 168 94 62 80.7 4
## 327 0 31 2 77 128 171 97 49 84.5 0
## 328 0 43 2 83 178 138 69 56 70.6 0
## 329 1 48 1 116 125 236 150 36 83.0 3
## 330 1 49 2 89 90 257 168 53 79.0 0
## 331 1 52 1 88 120 187 129 45 74.0 0
## 332 1 58 1 122 270 239 153 45 84.0 0
## 333 1 34 1 86 110 165 125 33 68.0 0
## 334 1 46 2 83 90 248 139 91 64.0 0
## 335 1 51 2 102 108 181 105 69 78.0 0
## 336 1 46 2 78 115 227 141 79 80.0 3
## 337 1 51 2 83 186 195 106 76 82.0 0
## 338 1 28 1 78 43 189 104 37 97.0 0
## 339 1 44 2 95 100 190 120 61 83.0 4
## 340 1 64 1 92 138 211 114 37 82.0 0
## 341 1 57 2 93 199 219 132 43 85.0 4
## 342 1 31 1 83 108 134 89 42 60.9 4
## 343 1 42 2 94 103 173 124 44 107.0 4
## 344 1 49 2 101 90 217 160 36 81.6 3
## 345 1 28 2 91 118 188 127 49 115.0 3
## 346 1 60 2 85 100 192 119 55 83.0 0
## 347 1 51 2 155 308 233 153 51 80.8 0
## 348 1 60 1 81 111 201 128 54 70.7 0
## 349 1 64 2 100 213 237 107 56 70.5 0
## 350 1 41 2 89 116 205 122 46 86.0 4
## 351 1 47 2 84 172 211 140 45 87.0 4
## 352 1 61 2 96 123 215 153 51 84.0 0
## 353 1 63 1 93 153 206 126 46 75.0 0
## 354 1 45 1 88 145 195 123 46 87.0 0
## 355 1 57 1 232 468 186 114 37 79.0 0
## 356 1 25 2 89 95 227 153 48 90.4 4
## 357 1 50 2 104 149 158 93 54 105.0 0
## 358 1 38 2 82 112 182 120 39 83.5 4
## 359 1 53 1 92 89 241 173 54 90.4 0
## 360 1 29 1 89 88 180 111 32 85.0 0
## 361 1 45 2 86 120 180 109 59 96.7 4
## 362 1 43 2 76 116 239 177 48 94.0 4
## 363 1 41 1 86 86 188 127 46 70.0 4
## 364 1 53 2 91 134 181 113 49 97.0 3
## 365 1 50 1 85 234 203 143 56 76.5 3
## 366 1 52 1 91 163 269 187 45 80.6 3
## 367 1 31 1 88 105 208 144 41 68.0 3
## 368 1 37 2 89 121 204 115 64 80.9 0
## 369 1 43 1 85 120 175 119 38 100.0 3
## 370 1 51 1 98 116 212 145 52 60.9 0
## 371 1 36 2 81 157 203 118 67 50.5 3
## 372 1 27 1 89 91 180 118 28 60.7 4
## 373 1 25 2 88 101 194 125 59 60.9 0
## 374 1 57 2 82 145 222 138 52 80.4 4
## 375 1 36 1 88 148 474 135 45 100.2 0
## 376 1 47 1 82 88 204 126 41 80.5 0
## 377 1 30 1 85 56 130 86 33 60.9 0
## 378 1 40 2 82 138 181 121 52 70.7 4
## 379 1 45 2 86 136 216 147 54 90.1 4
## 380 1 59 1 93 82 196 95 42 70.4 0
## 381 1 61 1 86 162 203 103 48 60.8 0
## 382 1 55 2 86 113 247 158 69 90.5 4
## 383 1 38 1 98 165 210 136 42 90.5 0
## 384 1 41 2 93 109 189 93 73 60.6 4
## 385 1 27 1 84 119 181 114 48 70.6 4
## 386 1 30 1 86 89 176 111 33 70.0 0
## 387 1 38 2 93 141 198 131 51 84.0 4
## 388 1 46 2 151 356 227 169 48 90.4 0
## 389 1 37 1 89 87 165 112 38 73.0 0
## 390 1 52 2 77 161 214 138 42 91.0 3
## 391 1 41 1 79 119 198 114 37 96.0 4
## 392 1 50 2 126 228 223 149 55 84.0 4
## 393 1 58 1 97 206 180 113 35 97.0 0
## 394 1 45 2 81 142 293 229 37 102.0 4
## 395 1 58 1 91 89 240 135 48 75.0 0
## 396 1 31 2 82 87 179 118 46 75.0 4
## 397 1 48 2 247 507 281 176 52 93.0 4
## 398 1 40 1 78 86 249 171 44 78.0 3
## 399 1 35 1 80 121 192 127 42 82.0 0
## 400 1 43 1 96 108 170 110 50 75.0 4
## 401 1 41 2 74 124 205 132 41 98.0 4
## 402 1 28 2 93 129 170 106 54 92.0 4
## 403 1 48 2 100 162 252 197 48 89.0 0
## 404 1 32 2 92 129 182 113 55 82.0 3
## 405 1 38 2 78 168 191 124 50 65.0 4
## 406 1 61 1 81 150 177 114 44 100.0 0
## 407 1 31 2 85 118 203 129 50 80.0 3
## 408 1 50 2 116 276 208 133 57 85.0 3
## 409 1 51 2 92 166 274 185 36 73.0 4
## 410 1 36 2 78 107 172 97 64 82.0 4
## 411 1 38 1 94 65 189 126 39 82.0 0
## 412 1 43 2 85 148 221 143 69 81.0 4
## 413 1 26 2 86 90 208 139 55 70.0 0
## 414 1 59 1 91 123 241 148 53 70.7 0
## 415 1 38 2 83 193 213 143 48 80.3 4
## 416 1 35 1 89 93 138 79 44 80.6 3
## 417 1 30 2 85 118 203 129 50 80.0 3
## 418 1 26 2 74 116 166 87 70 60.5 4
## 419 1 33 1 98 127 222 155 55 80.2 0
## 420 1 27 1 84 84 111 67 38 60.5 0
## 421 1 39 1 85 95 244 184 51 60.8 0
## 422 1 57 1 87 155 190 130 32 90.8 4
## 423 1 28 1 83 116 202 150 43 90.0 0
## 424 1 48 2 131 185 189 121 58 60.3 3
## 425 1 37 1 89 149 170 107 53 60.1 0
## 426 1 48 2 259 322 197 144 44 80.7 4
## 427 1 28 1 81 92 201 138 38 70.4 0
## 428 1 26 1 79 89 146 88 51 70.0 4
## 429 1 33 1 88 120 133 94 35 60.5 3
## 430 1 51 2 231 254 265 171 52 80.8 0
## 431 1 43 2 77 115 185 130 55 60.7 4
## 432 1 36 2 81 137 194 133 48 80.4 3
## 433 1 31 2 84 76 130 85 41 70.1 0
## 434 1 33 1 87 95 221 143 54 71.0 3
## 435 1 39 2 78 114 234 163 52 70.6 3
## 436 1 30 1 79 83 178 125 45 80.4 0
## 437 1 29 1 80 85 152 100 34 90.6 0
## 438 1 27 2 88 93 172 101 60 60.2 4
## 439 1 58 1 87 93 170 78 34 90.0 4
## 440 1 39 1 86 93 184 126 32 80.5 4
## 441 1 48 2 83 117 253 188 54 80.0 3
## 442 1 31 2 82 118 181 114 58 60.7 0
## 443 1 45 2 94 126 183 88 72 64.0 4
## 444 1 54 2 95 121 210 133 51 85.0 3
## 445 1 52 2 123 204 296 139 51 91.0 4
## 446 1 26 1 73 111 152 94 38 74.0 3
## 447 1 61 1 79 158 181 110 43 65.0 4
## 448 1 38 2 75 134 229 153 45 81.0 3
## 449 1 38 2 86 112 170 99 44 83.0 0
## 450 1 42 1 66 121 188 138 45 71.0 0
## 451 1 57 2 145 347 270 200 40 82.5 0
## 452 1 51 2 183 380 253 171 46 65.0 0
## 453 1 49 2 71 86 193 107 58 66.0 4
## 454 1 38 2 68 137 247 174 56 89.0 3
## 455 1 56 2 116 262 274 174 75 97.0 3
## 456 1 50 2 90 121 222 140 50 83.0 4
## 457 1 45 1 103 181 192 83 35 89.0 0
## 458 1 41 2 100 120 252 183 55 93.0 0
## 459 1 52 2 82 105 206 137 43 81.5 4
## 460 1 52 1 87 191 262 125 38 84.0 3
## 461 1 54 2 102 142 200 120 50 101.0 3
## 462 1 45 2 90 137 183 113 50 109.0 4
## 463 1 34 2 88 120 221 146 39 85.0 0
## 464 1 44 2 68 94 210 117 59 103.5 3
## 465 1 40 2 77 132 188 102 50 103.0 3
## 466 1 33 2 75 104 210 120 63 71.5 0
## 467 1 57 2 111 179 222 136 60 70.0 0
## 468 1 49 2 93 118 239 145 59 77.0 0
## 469 1 52 2 84 60 207 140 42 78.0 0
## 470 1 48 1 89 95 177 101 54 68.0 0
## 471 1 49 1 118 174 184 101 56 60.0 3
## 472 1 38 2 83 132 209 128 61 86.0 3
## 473 1 55 2 67 159 246 157 57 83.0 4
## 474 1 47 1 92 136 190 115 52 79.0 3
## 475 1 50 2 78 150 131 63 55 73.0 3
## 476 1 62 1 72 139 239 177 48 90.0 3
## 477 1 35 1 74 124 182 105 54 68.5 0
## 478 1 47 1 89 139 237 84 47 84.0 0
## 479 1 40 2 77 148 195 102 50 87.6 0
## 480 1 49 2 90 130 245 145 38 80.5 3
## 481 1 55 2 72 64 205 128 50 62.0 4
## 482 1 42 2 74 105 152 85 45 68.0 4
## 483 1 42 2 81 131 234 159 56 83.5 0
## 484 1 46 1 86 94 272 167 79 71.0 0
## 485 1 57 1 88 138 212 126 56 82.0 3
## 486 1 54 2 143 190 205 129 51 91.0 3
## 487 1 56 1 173 311 127 59 49 69.0 0
## 488 1 45 2 72 129 168 77 59 58.0 0
## 489 1 46 2 80 124 204 122 52 101.0 0
## 490 1 57 2 89 113 232 143 62 84.0 0
## 491 1 40 2 74 142 200 117 51 88.0 3
## 492 1 49 2 88 117 206 114 52 90.0 3
## 493 1 26 2 76 87 152 76 57 76.0 4
## 494 1 35 2 82 89 175 94 55 69.0 0
## 495 1 49 2 95 124 211 128 64 93.0 3
## 496 1 38 2 86 147 189 127 36 85.0 0
## 497 1 44 2 78 113 173 101 64 83.0 0
## 498 1 53 2 295 570 164 92 53 83.0 0
## 499 1 34 1 79 114 214 111 39 79.0 0
## 500 1 33 1 84 95 154 85 59 68.0 0
## 501 1 33 2 85 149 225 136 40 81.0 3
## 502 1 59 2 83 107 221 137 55 99.0 0
## 503 1 35 1 89 136 197 123 35 94.0 3
## 504 1 48 2 82 103 225 147 55 90.0 0
## 505 1 40 1 91 77 212 150 40 87.0 0
## 506 1 37 2 82 110 145 76 45 76.0 4
## 507 1 38 2 313 347 188 109 43 94.0 0
## 508 1 38 2 79 98 195 125 46 74.0 3
## 509 1 35 2 85 118 203 129 50 80.0 3
## 510 1 30 2 83 98 206 136 51 71.0 0
## 511 1 60 2 104 167 220 152 42 85.0 0
## 512 1 32 2 81 118 195 117 58 84.0 4
## 513 1 38 2 89 198 226 119 79 75.0 0
## 514 1 62 1 80 177 193 100 42 90.0 3
## 515 1 38 2 131 287 180 99 42 85.0 0
## 516 1 49 2 82 174 243 163 39 93.0 3
## 517 1 35 2 87 130 207 143 62 83.0 0
## 518 1 45 1 81 118 166 87 51 75.0 4
## 519 1 48 2 86 132 275 192 51 86.0 4
## 520 1 33 2 74 92 207 142 55 69.0 3
## 521 1 46 2 93 134 259 179 50 96.0 4
## 522 1 49 2 103 131 272 189 60 75.0 3
## 523 1 47 2 83 95 244 156 65 71.0 4
## 524 1 63 2 91 133 242 155 56 86.0 3
## 525 1 41 2 78 102 214 138 72 85.2 0
## 526 1 27 2 86 89 131 64 56 75.0 0
## 527 1 27 2 88 94 201 138 50 76.0 0
## 528 1 36 2 78 94 212 127 71 66.0 0
## 529 1 53 2 83 122 223 134 67 89.5 3
## 530 1 30 2 86 102 165 106 51 76.0 0
## 531 1 26 2 79 164 158 90 58 77.5 3
## 532 1 52 2 89 247 234 171 49 98.0 0
## 533 1 42 2 76 142 289 184 87 81.5 3
## 534 1 44 2 81 213 235 181 47 83.5 0
## 535 1 44 1 88 60 202 149 36 90.0 0
## 536 1 48 1 70 128 177 106 31 94.0 0
## 537 1 42 2 85 108 199 152 38 73.0 0
## 538 1 49 1 82 119 169 112 38 71.5 0
## 539 1 60 1 170 277 195 139 42 102.0 0
## 540 1 43 2 83 93 218 141 63 85.0 4
## 541 1 53 1 202 302 219 161 39 78.0 4
## 542 1 37 2 82 126 209 143 57 85.0 0
## 543 1 36 2 77 212 255 163 47 69.0 0
## 544 1 49 2 89 116 176 131 40 72.0 4
## 545 1 28 2 75 124 270 174 48 99.0 3
## 546 1 53 2 92 142 331 237 46 83.0 3
## 547 1 62 1 125 286 205 127 28 100.0 4
## 548 1 54 2 100 132 283 190 55 82.0 0
## 549 1 44 1 79 66 196 142 45 66.0 3
## 550 1 42 2 74 98 159 108 44 72.5 3
## 551 1 48 2 88 148 275 161 41 93.0 4
## 552 1 58 2 97 178 214 137 64 56.0 3
## 553 1 44 1 92 143 199 137 41 96.0 0
## 554 1 32 2 83 108 238 175 59 86.0 0
## 555 1 26 1 107 106 206 126 47 75.0 0
## 556 1 37 2 94 118 219 157 51 70.0 4
## 557 1 32 2 75 124 276 204 48 75.0 4
## 558 1 33 2 68 105 163 101 55 77.0 3
## 559 1 27 2 73 101 267 210 55 79.0 4
## 560 1 52 2 90 157 219 148 62 88.0 4
## 561 1 42 2 91 138 221 86 54 80.1 0
## 562 1 37 2 79 98 165 100 49 76.0 0
## 563 1 38 1 94 162 257 154 40 89.0 3
## 564 1 29 1 74 93 159 92 49 76.5 0
## 565 1 35 2 77 95 205 141 39 80.0 0
## 566 1 47 1 83 89 189 113 39 79.0 0
## 567 1 32 2 91 116 173 100 47 87.7 0
## 568 1 54 2 89 150 290 208 56 88.1 0
## 569 1 42 2 79 208 229 158 50 87.0 4
## 570 1 51 1 77 93 266 174 65 85.9 4
## 571 1 33 2 86 99 146 89 40 72.0 3
## 572 1 64 1 87 134 217 150 39 82.0 0
## 573 1 61 2 137 154 233 143 48 102.5 3
## 574 1 44 2 92 180 172 87 53 70.5 0
## 575 1 46 2 79 116 243 155 42 70.0 4
## 576 1 35 2 83 121 181 102 54 60.0 0
## 577 1 43 1 78 30 167 89 58 79.0 4
## 578 1 49 2 82 72 191 102 56 76.0 3
## 579 1 39 1 84 70 194 135 41 75.0 3
## 580 1 32 2 76 116 205 135 39 81.5 0
## 581 1 62 1 69 98 229 157 45 71.0 4
## 582 1 55 2 96 119 179 93 67 82.0 3
## 583 1 42 1 73 161 252 175 35 87.0 3
## 584 1 41 2 71 154 259 185 62 89.0 3
## 585 1 63 1 178 382 271 146 39 90.5 0
## 586 1 52 2 80 109 180 92 59 78.0 0
## 587 1 39 1 81 84 191 132 42 79.0 0
## 588 1 60 1 65 104 171 101 50 78.0 3
## 589 1 59 2 86 136 304 202 61 71.0 4
## 590 1 34 1 70 93 192 125 47 72.0 3
## 591 1 40 2 79 121 209 127 67 71.0 4
## 592 1 48 2 89 85 211 144 50 75.0 0
## 593 1 55 1 78 152 227 115 47 81.0 0
## 594 1 42 2 86 121 214 135 61 81.0 0
## 595 1 27 2 61 80 166 101 51 70.0 3
## 596 1 39 2 74 97 178 107 52 81.0 0
## 597 1 27 1 78 123 216 146 44 83.0 4
## 598 1 46 2 84 99 206 142 55 83.0 3
## 599 1 49 1 80 151 163 104 47 61.0 4
## 600 1 58 1 77 93 216 144 50 81.0 3
## 601 1 31 2 73 118 149 92 45 77.0 0
## 602 1 42 1 76 91 171 112 42 81.0 4
## 603 1 40 2 80 85 201 122 72 83.0 0
## 604 1 40 2 81 147 200 113 83 67.0 4
## 605 1 41 1 71 138 121 54 53 61.0 0
## 606 1 46 1 82 82 214 140 43 71.0 0
## 607 1 48 2 100 132 221 128 58 85.0 0
## 608 1 48 2 99 164 248 146 79 90.0 0
## 609 1 26 2 78 127 202 127 66 80.0 0
## 610 1 33 1 84 131 188 134 47 78.0 0
## 611 1 55 1 151 199 177 101 31 98.0 4
## 612 1 46 2 90 159 222 136 47 107.0 0
## 613 1 54 2 91 78 188 101 68 73.0 3
## 614 1 51 1 74 75 164 109 44 60.0 3
## 615 1 37 1 93 160 210 134 46 89.0 4
## 616 1 48 2 84 145 240 152 54 77.0 0
## 617 1 31 2 76 178 209 143 49 97.0 0
## 618 1 54 2 82 168 269 179 50 80.0 0
## 619 1 30 2 67 107 188 115 64 58.5 4
## 620 1 57 1 73 64 183 100 55 84.0 3
## 621 1 36 2 79 141 252 161 46 82.0 4
## 622 1 37 2 76 102 252 190 56 88.0 3
## 623 1 51 2 98 201 216 131 54 62.0 3
## 624 1 53 2 97 119 205 125 58 89.0 0
## 625 1 50 1 86 134 237 159 47 89.0 0
## 626 1 42 2 86 130 226 148 61 93.0 0
## 627 1 35 2 67 142 211 135 55 75.0 0
## 628 1 56 1 95 123 235 178 33 95.5 3
## 629 1 45 1 90 103 252 138 31 83.0 3
## 630 1 37 2 77 87 171 111 52 71.0 0
## 631 1 51 2 109 181 178 115 58 71.0 0
## 632 1 44 1 74 94 197 147 39 69.0 0
## 633 1 39 2 81 152 298 204 69 80.0 0
## 634 1 45 1 84 103 229 150 76 88.0 0
## 635 1 40 2 70 144 197 134 41 89.0 3
## 636 1 42 2 82 133 251 192 53 78.0 4
## 637 1 43 2 87 151 205 110 79 71.5 4
## 638 1 30 2 76 99 241 157 65 61.0 4
## 639 1 55 2 81 172 257 170 42 85.0 4
## 640 1 59 2 75 122 202 130 55 77.0 4
## 641 1 61 2 88 172 212 135 52 68.0 3
## 642 1 46 1 75 157 182 136 42 103.0 0
## 643 1 43 2 83 162 228 161 40 80.0 4
## 644 1 35 2 79 109 177 96 70 90.0 0
## 645 1 41 1 90 142 234 179 38 109.0 4
## 646 1 35 2 102 95 195 137 41 82.0 4
## 647 1 35 2 89 133 232 150 59 87.0 4
## 648 1 41 2 70 87 166 103 51 85.5 4
## 649 1 36 2 84 91 157 89 56 74.5 0
## 650 1 48 1 101 125 175 131 39 99.8 4
## 651 1 45 2 354 406 215 169 44 95.8 0
## 652 1 60 1 82 167 216 134 61 70.1 4
## 653 1 26 2 85 118 203 129 50 80.0 4
## 654 1 31 2 85 119 156 92 52 84.5 0
## 655 1 35 2 83 135 141 89 37 89.1 3
## 656 1 50 2 93 118 212 126 73 97.0 4
## 657 1 26 2 85 118 203 129 50 80.0 4
## 658 1 26 2 89 137 152 87 56 80.0 3
## 659 1 38 2 72 169 148 85 55 92.0 0
## Lingkar_Perut Target_Hipertensi Nyeri_Dada Riwayat_Keluarga IMT
## 1 90.0 0 0 0 24.73246
## 2 76.0 0 0 0 21.07725
## 3 84.0 0 0 0 19.32349
## 4 68.0 0 0 0 20.15818
## 5 89.0 0 0 0 31.63032
## 6 67.0 1 0 0 18.27593
## 7 87.0 0 0 0 25.41397
## 8 76.0 0 0 0 23.65872
## 9 81.5 0 0 0 24.66869
## 10 86.0 1 0 0 25.49015
## 11 84.0 1 0 0 29.20116
## 12 99.0 0 0 0 33.88957
## 13 85.0 1 1 1 26.84564
## 14 97.0 1 0 0 31.24335
## 15 91.0 0 0 0 26.28166
## 16 84.0 1 1 1 28.95399
## 17 91.0 0 0 0 26.22837
## 18 88.0 0 0 0 25.39309
## 19 80.0 1 0 0 23.77089
## 20 90.0 1 0 0 27.61623
## 21 100.0 0 0 0 32.32478
## 22 95.0 0 0 0 26.63285
## 23 84.0 0 0 0 25.56213
## 24 84.0 0 0 0 21.02385
## 25 75.0 0 0 0 20.93212
## 26 84.0 0 1 1 29.75685
## 27 85.0 0 0 0 28.27047
## 28 101.0 0 0 0 33.14854
## 29 94.0 0 1 1 26.97778
## 30 84.0 0 0 0 17.52457
## 31 75.5 0 0 0 21.56715
## 32 87.0 0 0 0 28.82753
## 33 84.0 0 0 0 19.86061
## 34 84.0 0 0 0 18.44000
## 35 84.0 1 0 0 21.75132
## 36 84.0 0 0 0 29.01369
## 37 84.0 0 0 0 17.92088
## 38 100.0 0 0 0 27.86485
## 39 84.0 1 1 1 29.75279
## 40 84.0 0 0 0 21.91572
## 41 96.0 0 0 0 28.54525
## 42 66.5 0 1 1 18.86095
## 43 86.0 0 0 0 25.58845
## 44 77.5 1 0 0 24.32852
## 45 108.0 0 0 0 29.86706
## 46 94.0 0 1 1 27.23101
## 47 75.0 1 1 1 19.90233
## 48 85.0 1 0 0 26.69084
## 49 104.0 0 0 0 36.74632
## 50 84.0 0 0 0 25.43934
## 51 76.0 0 0 0 23.15555
## 52 84.0 0 0 0 22.78705
## 53 84.0 0 0 0 34.32847
## 54 73.0 1 0 0 20.18031
## 55 84.0 0 1 1 20.10000
## 56 84.0 0 0 0 27.15466
## 57 58.0 1 0 0 19.73156
## 58 86.0 0 0 0 27.03135
## 59 102.0 1 0 0 32.49108
## 60 84.0 1 0 0 18.78906
## 61 70.0 0 0 0 23.15688
## 62 84.0 0 0 0 24.35169
## 63 88.0 1 0 0 29.48870
## 64 96.0 1 0 0 27.26497
## 65 94.5 1 0 0 29.38902
## 66 97.0 0 0 0 28.87297
## 67 84.0 0 0 0 21.77041
## 68 84.0 0 0 0 27.07079
## 69 78.0 1 0 0 21.39438
## 70 84.0 0 0 0 21.11944
## 71 81.0 1 0 0 22.55370
## 72 81.0 0 0 0 25.08428
## 73 106.0 1 0 0 36.04265
## 74 84.0 0 0 0 29.33175
## 75 76.0 0 0 0 21.89586
## 76 85.0 0 0 0 23.74495
## 77 75.0 0 0 0 23.20529
## 78 75.0 1 0 0 23.15229
## 79 78.0 0 0 0 19.04832
## 80 80.5 1 0 0 26.83518
## 81 81.0 1 0 0 24.59713
## 82 76.0 0 0 0 20.85728
## 83 94.0 1 0 0 28.12500
## 84 84.0 1 0 0 29.15555
## 85 77.0 0 0 0 26.80059
## 86 105.0 0 1 1 26.49790
## 87 84.0 0 0 0 27.61073
## 88 70.0 1 0 0 18.83970
## 89 79.0 1 0 0 22.14791
## 90 64.0 0 1 1 20.06141
## 91 94.0 1 0 0 37.18201
## 92 84.0 1 0 0 17.67570
## 93 89.0 0 0 0 23.45660
## 94 65.0 1 1 1 17.41497
## 95 75.0 0 0 0 23.13440
## 96 94.0 1 0 0 29.35537
## 97 67.0 1 0 0 16.89624
## 98 73.0 1 0 0 19.85246
## 99 80.0 1 0 0 24.82045
## 100 89.0 0 1 1 27.41480
## 101 73.0 0 0 0 18.58326
## 102 84.0 1 0 0 14.63534
## 103 78.5 1 0 0 23.88556
## 104 77.0 0 0 0 20.68906
## 105 84.0 1 0 0 28.20052
## 106 87.0 1 0 0 23.33768
## 107 67.0 0 0 0 19.14062
## 108 75.0 1 0 0 19.47969
## 109 84.0 0 1 1 24.57752
## 110 84.0 1 0 0 26.66034
## 111 121.0 0 0 0 37.41453
## 112 80.0 0 0 0 25.93025
## 113 85.0 0 0 0 22.91779
## 114 91.5 0 0 0 27.05906
## 115 94.0 1 1 1 31.75952
## 116 84.0 0 0 0 30.34151
## 117 68.0 0 0 0 18.34226
## 118 84.0 1 0 0 23.88369
## 119 93.0 0 0 0 29.91876
## 120 94.0 0 0 0 28.17838
## 121 89.0 1 0 0 29.80800
## 122 87.0 0 0 0 27.65529
## 123 67.0 1 0 0 23.83517
## 124 84.0 1 0 0 21.43478
## 125 78.0 1 1 1 21.17022
## 126 84.0 1 0 0 17.91232
## 127 77.0 1 0 0 27.60596
## 128 83.0 1 0 0 21.27465
## 129 93.0 1 0 0 26.39407
## 130 86.0 0 0 0 24.75762
## 131 84.0 0 0 0 23.91784
## 132 84.0 1 0 0 20.85728
## 133 84.0 0 0 0 44.37798
## 134 84.0 0 0 0 23.85016
## 135 85.0 0 0 0 22.65909
## 136 84.0 0 0 0 27.36466
## 137 79.5 0 0 0 21.34146
## 138 100.9 1 0 0 34.08479
## 139 103.0 0 0 0 28.50396
## 140 85.0 0 0 0 30.07390
## 141 91.0 1 0 0 30.35975
## 142 85.0 0 0 0 26.34222
## 143 96.0 1 0 0 26.10024
## 144 84.0 0 0 0 20.23294
## 145 87.0 1 0 0 29.40128
## 146 84.0 0 0 0 22.25125
## 147 70.0 1 0 0 20.68966
## 148 93.0 1 0 0 28.24219
## 149 95.0 1 0 0 29.22404
## 150 82.0 1 0 0 26.31948
## 151 83.0 0 0 0 23.30905
## 152 84.0 1 0 0 27.41584
## 153 66.0 0 0 0 21.33830
## 154 93.0 1 0 0 25.55556
## 155 67.5 1 0 0 18.76370
## 156 70.0 1 0 0 20.53147
## 157 77.0 1 0 0 21.17575
## 158 80.1 0 0 0 21.89958
## 159 70.1 0 0 0 18.73396
## 160 87.0 1 0 0 23.39582
## 161 82.0 1 0 0 24.75320
## 162 95.0 0 0 0 31.92515
## 163 103.0 0 1 1 37.65465
## 164 84.0 0 0 0 30.30549
## 165 73.0 0 0 0 23.91784
## 166 81.0 1 0 0 22.08414
## 167 87.0 0 0 0 23.82222
## 168 84.0 0 0 0 23.97503
## 169 77.0 0 0 0 21.16307
## 170 83.0 0 0 0 27.41940
## 171 93.0 0 0 0 29.63836
## 172 81.0 1 0 0 27.16270
## 173 84.0 0 0 0 26.39581
## 174 84.0 1 0 0 16.71121
## 175 58.0 0 1 1 16.75035
## 176 84.0 0 0 0 20.72940
## 177 94.0 0 0 0 27.17456
## 178 84.0 0 0 0 23.26118
## 179 84.0 0 0 0 25.00000
## 180 62.0 0 0 0 16.12497
## 181 62.0 1 0 0 17.79243
## 182 78.0 0 1 1 23.73324
## 183 84.0 1 0 0 16.91954
## 184 87.0 0 0 0 29.50835
## 185 91.0 0 0 0 28.58617
## 186 88.0 0 0 0 27.87989
## 187 84.0 0 0 0 25.19629
## 188 84.0 0 0 0 24.52783
## 189 70.0 0 0 0 19.70351
## 190 87.5 1 0 0 28.85991
## 191 73.0 0 0 0 25.70918
## 192 81.0 1 0 0 26.13920
## 193 59.0 0 0 0 15.95845
## 194 84.0 1 0 0 25.81337
## 195 84.0 1 0 0 17.02735
## 196 86.0 1 0 0 29.85147
## 197 77.0 1 0 0 20.54765
## 198 84.0 1 0 0 24.50340
## 199 90.0 0 0 0 32.35673
## 200 98.0 0 0 0 30.11999
## 201 84.0 0 0 0 20.76886
## 202 70.5 1 0 0 22.37708
## 203 83.0 0 0 0 23.92569
## 204 84.0 0 0 0 19.35441
## 205 84.0 0 0 0 25.58821
## 206 96.0 1 1 1 36.32373
## 207 79.0 1 0 0 21.79647
## 208 98.0 0 0 0 28.51223
## 209 77.0 1 0 0 19.03161
## 210 91.0 1 0 0 27.82206
## 211 100.0 1 0 0 31.48098
## 212 84.0 0 0 0 23.58880
## 213 80.0 0 1 1 23.52102
## 214 85.0 0 0 0 26.83518
## 215 88.0 1 1 1 24.77449
## 216 84.0 0 1 1 30.26700
## 217 71.0 1 0 0 17.71610
## 218 68.0 1 0 0 22.90702
## 219 88.0 1 0 0 25.94478
## 220 75.0 0 0 0 19.43386
## 221 84.0 1 0 0 31.41863
## 222 67.0 0 0 0 19.93550
## 223 84.0 0 0 0 21.04683
## 224 84.0 1 0 0 27.76710
## 225 84.0 1 0 0 27.45502
## 226 67.0 1 0 0 20.21047
## 227 79.0 1 0 0 24.26514
## 228 98.0 1 0 0 34.01173
## 229 84.0 0 0 0 24.78073
## 230 84.0 0 0 0 26.36054
## 231 97.0 1 0 0 27.26871
## 232 84.0 1 0 0 22.17970
## 233 84.0 0 1 1 24.39660
## 234 84.0 1 0 0 23.90791
## 235 83.5 0 0 0 24.09801
## 236 73.0 1 1 1 20.13478
## 237 86.0 1 0 0 26.17054
## 238 81.0 0 0 0 23.48600
## 239 64.0 1 0 0 18.45714
## 240 84.0 1 0 0 18.29251
## 241 77.0 0 0 0 25.14449
## 242 99.0 1 0 0 32.09460
## 243 64.0 0 0 0 17.68834
## 244 99.0 0 0 0 26.89879
## 245 78.0 1 0 0 24.35126
## 246 91.0 1 0 0 27.19617
## 247 80.8 0 0 0 21.72201
## 248 84.0 0 0 0 29.94036
## 249 80.0 0 0 0 21.46205
## 250 84.0 1 0 0 24.58092
## 251 84.0 1 0 0 25.96293
## 252 84.0 0 0 0 27.03000
## 253 66.0 1 0 0 21.81441
## 254 101.0 0 0 0 28.95964
## 255 84.0 0 0 0 20.17455
## 256 84.0 0 0 0 20.57613
## 257 71.0 0 0 0 20.92014
## 258 68.0 0 0 0 23.09902
## 259 75.0 1 0 0 20.89686
## 260 79.0 1 1 1 25.58845
## 261 98.0 0 0 0 27.75109
## 262 84.0 0 0 0 18.71094
## 263 64.0 1 0 0 21.03532
## 264 84.0 0 0 0 28.67231
## 265 58.0 1 0 0 18.18408
## 266 80.0 1 0 0 21.44224
## 267 77.0 1 0 0 23.65725
## 268 105.0 0 0 0 30.68399
## 269 84.0 0 1 1 32.67653
## 270 88.0 0 0 0 24.55536
## 271 84.0 0 0 0 21.03509
## 272 77.5 1 0 0 21.65764
## 273 98.0 1 0 0 33.32151
## 274 85.0 1 0 0 37.08642
## 275 94.0 1 0 0 27.12032
## 276 92.0 1 0 0 26.28080
## 277 84.0 1 0 0 25.35869
## 278 81.0 0 0 0 24.61715
## 279 63.0 0 0 0 18.96311
## 280 79.0 1 0 0 22.91802
## 281 72.0 1 0 0 20.62253
## 282 84.0 0 0 0 19.99796
## 283 68.0 0 0 0 19.69356
## 284 99.0 1 0 0 31.15339
## 285 92.0 0 0 0 28.77368
## 286 77.0 0 0 0 24.47523
## 287 87.0 0 0 0 27.18655
## 288 77.0 1 0 0 20.06095
## 289 83.5 0 0 0 22.25977
## 290 72.0 0 1 1 22.10432
## 291 91.0 1 0 0 30.54011
## 292 84.0 0 0 0 30.18543
## 293 78.0 0 0 0 18.76525
## 294 104.0 1 0 0 33.14723
## 295 87.0 0 0 0 23.66496
## 296 85.0 1 0 0 26.15670
## 297 82.0 0 0 0 29.13583
## 298 90.0 0 0 0 35.10290
## 299 86.0 1 1 1 27.29692
## 300 84.0 0 0 0 20.36002
## 301 95.0 0 0 0 28.81728
## 302 69.0 1 0 0 19.03001
## 303 89.0 1 0 0 31.88592
## 304 98.0 1 1 1 32.58629
## 305 84.0 0 0 0 23.93005
## 306 61.0 0 1 1 18.07372
## 307 91.0 0 0 0 29.11842
## 308 76.0 0 0 0 20.34024
## 309 91.0 0 0 0 27.66174
## 310 70.0 1 0 0 21.54812
## 311 84.0 1 0 0 23.49446
## 312 94.5 0 0 0 27.02431
## 313 86.0 0 1 1 27.46562
## 314 84.0 1 0 0 28.08920
## 315 64.0 0 0 0 17.50388
## 316 85.0 1 0 0 32.77903
## 317 73.0 0 1 1 24.53392
## 318 84.0 0 1 1 22.90951
## 319 78.0 0 0 0 24.11032
## 320 107.0 1 0 0 30.20349
## 321 84.0 0 0 0 26.66205
## 322 86.0 0 0 0 24.82308
## 323 72.0 0 0 0 19.70756
## 324 83.0 1 1 1 23.95479
## 325 97.0 1 0 0 27.62510
## 326 89.0 1 0 0 27.66767
## 327 84.0 1 0 0 25.43413
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## 651 90.0 1 0 0 24.86008
## 652 84.0 1 1 1 16.53948
## 653 84.0 1 0 0 25.00000
## 654 84.0 0 1 1 24.28694
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## 656 84.0 1 1 1 27.87396
## 657 84.0 0 0 0 25.00000
## 658 81.2 1 0 0 23.05047
## 659 93.0 1 0 0 27.93422# Menghitung proporsi kelas
prop_table <- prop.table(table(undersampled_reglog$Target_PJK))
print(prop_table)##
## 0 1
## 0.4977238 0.5022762table(undersampled_reglog$Target_PJK)##
## 0 1
## 328 331reglog_train <- glm(Target_PJK ~ ., data = train_reglog, family = binomial(link = "logit"))
summ <- summary(smote_reglog)
summ## Target_PJK Age Sex GD_Puasa GD_PP
## 0:1500 Min. :22.00 1:1023 Min. : 56.0 Min. : 0.0
## 1:1500 1st Qu.:35.76 2:1977 1st Qu.: 80.0 1st Qu.:101.0
## Median :43.32 Median : 85.0 Median :119.0
## Mean :43.44 Mean : 91.1 Mean :132.9
## 3rd Qu.:51.00 3rd Qu.: 91.0 3rd Qu.:143.0
## Max. :65.00 Max. :427.0 Max. :570.0
## Kolestrol LDL HDL Lipoprotein Merokok
## Min. :101.0 Min. : 22.0 Min. :22.00 Min. : 49.2 0:1363
## 1st Qu.:180.7 1st Qu.:109.5 1st Qu.:43.00 1st Qu.: 72.0 3: 720
## Median :202.5 Median :129.0 Median :50.00 Median : 80.5 4: 917
## Mean :204.0 Mean :130.0 Mean :50.37 Mean : 80.5
## 3rd Qu.:224.5 3rd Qu.:148.0 3rd Qu.:56.00 3rd Qu.: 88.0
## Max. :474.0 Max. :286.0 Max. :94.00 Max. :120.0
## Lingkar_Perut Target_Hipertensi Nyeri_Dada Riwayat_Keluarga
## Min. : 54.00 0:1313 Min. :0.0000 0:2454
## 1st Qu.: 80.00 1:1687 1st Qu.:0.0000 1: 546
## Median : 84.00 Median :0.0000
## Mean : 83.87 Mean :0.1822
## 3rd Qu.: 88.41 3rd Qu.:0.0000
## Max. :128.00 Max. :1.0000
## IMT
## Min. :13.90
## 1st Qu.:21.58
## Median :24.80
## Mean :24.92
## 3rd Qu.:27.69
## Max. :44.63pred1 <- predict(reglog_train, test_reglog, type = "response")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleadingpredicted <- round(pred1)
tab <- table(Predicted = predicted, Reference = test_reglog$Target_PJK)
tab## Reference
## Predicted 0 1
## 0 1456 141confusionMatrix(as.factor(predicted), test_reglog$Target_PJK)## Warning in confusionMatrix.default(as.factor(predicted),
## test_reglog$Target_PJK): Levels are not in the same order for reference and
## data. Refactoring data to match.## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1456 141
## 1 0 0
##
## Accuracy : 0.9117
## 95% CI : (0.8967, 0.9252)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 0.5224
##
## Kappa : 0
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 1.0000
## Specificity : 0.0000
## Pos Pred Value : 0.9117
## Neg Pred Value : NaN
## Prevalence : 0.9117
## Detection Rate : 0.9117
## Detection Prevalence : 1.0000
## Balanced Accuracy : 0.5000
##
## 'Positive' Class : 0
## 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':
##
## recodereglog_smote <- glm(Target_PJK ~ ., data = smote_reglog, family = binomial(link = "logit"))
summ <- summary(reglog_smote)
summ##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = smote_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.95548 -1.05946 0.00894 1.05710 1.89182
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.816931 0.480789 -1.699 0.089292 .
## Age -0.015621 0.004421 -3.533 0.000411 ***
## Sex2 0.003235 0.095677 0.034 0.973029
## GD_Puasa -0.004814 0.002102 -2.290 0.022001 *
## GD_PP 0.004127 0.001109 3.721 0.000199 ***
## Kolestrol 0.003587 0.002423 1.481 0.138713
## LDL -0.001811 0.002671 -0.678 0.497801
## HDL -0.002830 0.004368 -0.648 0.516993
## Lipoprotein -0.008837 0.007309 -1.209 0.226624
## Merokok3 -0.024027 0.097421 -0.247 0.805196
## Merokok4 -0.358287 0.091148 -3.931 8.47e-05 ***
## Lingkar_Perut 0.016625 0.006393 2.600 0.009311 **
## Target_Hipertensi1 1.055156 0.078790 13.392 < 2e-16 ***
## Nyeri_Dada 0.897797 0.809793 1.109 0.267571
## Riwayat_Keluarga1 -0.134743 0.806804 -0.167 0.867363
## IMT -0.010831 0.018022 -0.601 0.547861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4158.9 on 2999 degrees of freedom
## Residual deviance: 3849.3 on 2984 degrees of freedom
## AIC: 3881.3
##
## Number of Fisher Scoring iterations: 4pred2 <- predict(reglog_smote, test_reglog, type = "response")
predicted <- round(pred2)
tab <- table(Predicted = predicted, Reference = test_reglog$Target_PJK)
tab## Reference
## Predicted 0 1
## 0 843 44
## 1 613 97confusionMatrix(as.factor(predicted), test_reglog$Target_PJK)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 843 44
## 1 613 97
##
## Accuracy : 0.5886
## 95% CI : (0.564, 0.6129)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0946
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.5790
## Specificity : 0.6879
## Pos Pred Value : 0.9504
## Neg Pred Value : 0.1366
## Prevalence : 0.9117
## Detection Rate : 0.5279
## Detection Prevalence : 0.5554
## Balanced Accuracy : 0.6335
##
## 'Positive' Class : 0
## library(car)
reglog_over <- glm(Target_PJK ~ ., data = oversampling_reglog, family = binomial(link = "logit"))
summ <- summary(reglog_over)
summ##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = oversampling_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.195 -1.044 -0.709 1.054 1.783
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8266481 0.3558659 -2.323 0.020183 *
## Age -0.0107637 0.0031938 -3.370 0.000751 ***
## Sex2 -0.1399089 0.0811503 -1.724 0.084695 .
## GD_Puasa -0.0017067 0.0012621 -1.352 0.176281
## GD_PP 0.0032372 0.0006258 5.173 2.3e-07 ***
## Kolestrol 0.0022579 0.0010639 2.122 0.033808 *
## LDL -0.0011337 0.0012702 -0.893 0.372079
## HDL 0.0040252 0.0030930 1.301 0.193121
## Lipoprotein -0.0033741 0.0036211 -0.932 0.351457
## Merokok3 -0.0225438 0.0880406 -0.256 0.797904
## Merokok4 -0.1942508 0.0813992 -2.386 0.017014 *
## Lingkar_Perut 0.0024789 0.0038123 0.650 0.515541
## Target_Hipertensi1 1.0863313 0.0702985 15.453 < 2e-16 ***
## Nyeri_Dada 0.0868888 0.1559416 0.557 0.577399
## Riwayat_Keluarga1 0.6662375 0.1800663 3.700 0.000216 ***
## IMT -0.0011392 0.0093392 -0.122 0.902913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5169.4 on 3728 degrees of freedom
## Residual deviance: 4776.3 on 3713 degrees of freedom
## AIC: 4808.3
##
## Number of Fisher Scoring iterations: 4pred3 <- predict(reglog_over, test_reglog, type = "response")
predicted <- round(pred3)
tab <- table(Predicted = predicted, Reference = test_reglog$Target_PJK)
tab## Reference
## Predicted 0 1
## 0 800 38
## 1 656 103confusionMatrix(as.factor(predicted), test_reglog$Target_PJK)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 800 38
## 1 656 103
##
## Accuracy : 0.5654
## 95% CI : (0.5407, 0.5899)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.094
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.5495
## Specificity : 0.7305
## Pos Pred Value : 0.9547
## Neg Pred Value : 0.1357
## Prevalence : 0.9117
## Detection Rate : 0.5009
## Detection Prevalence : 0.5247
## Balanced Accuracy : 0.6400
##
## 'Positive' Class : 0
## library(car)
reglog_under <- glm(Target_PJK ~ ., data = undersampled_reglog, family = binomial(link = "logit"))
summ <- summary(reglog_under)
summ##
## Call:
## glm(formula = Target_PJK ~ ., family = binomial(link = "logit"),
## data = undersampled_reglog)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8625 -1.0745 0.5467 1.0670 1.8351
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.149872 0.962740 -1.194 0.2323
## Age -0.013724 0.008967 -1.531 0.1259
## Sex2 0.031191 0.213544 0.146 0.8839
## GD_Puasa 0.001876 0.004353 0.431 0.6665
## GD_PP 0.001500 0.002295 0.654 0.5133
## Kolestrol 0.009760 0.005222 1.869 0.0616 .
## LDL -0.008204 0.005772 -1.421 0.1552
## HDL -0.012470 0.009333 -1.336 0.1815
## Lipoprotein -0.007909 0.015091 -0.524 0.6002
## Merokok3 0.077756 0.213077 0.365 0.7152
## Merokok4 -0.270456 0.193403 -1.398 0.1620
## Lingkar_Perut 0.021505 0.012973 1.658 0.0974 .
## Target_Hipertensi1 1.017008 0.168600 6.032 1.62e-09 ***
## Nyeri_Dada 0.965224 0.230393 4.189 2.80e-05 ***
## Riwayat_Keluarga1 NA NA NA NA
## IMT -0.030273 0.037349 -0.811 0.4176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 913.55 on 658 degrees of freedom
## Residual deviance: 840.51 on 644 degrees of freedom
## AIC: 870.51
##
## Number of Fisher Scoring iterations: 4pred4 <- predict(reglog_under, test_reglog, type = "response")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleadingpredicted <- round(pred4)
tab <- table(Predicted = predicted, Reference = test_reglog$Target_PJK)
tab## Reference
## Predicted 0 1
## 0 819 35
## 1 637 106confusionMatrix(as.factor(predicted), test_reglog$Target_PJK)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 819 35
## 1 637 106
##
## Accuracy : 0.5792
## 95% CI : (0.5546, 0.6036)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.1073
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.5625
## Specificity : 0.7518
## Pos Pred Value : 0.9590
## Neg Pred Value : 0.1427
## Prevalence : 0.9117
## Detection Rate : 0.5128
## Detection Prevalence : 0.5348
## Balanced Accuracy : 0.6571
##
## 'Positive' Class : 0
## library(e1071)## Warning: package 'e1071' was built under R version 4.2.3##
## Attaching package: 'e1071'## The following object is masked from 'package:gtools':
##
## permutationstuningsvm <- tune(svm, Target_PJK~.,data=smote_reglog,
ranges=list(kernel=c("radial","linear","polynomial","sigmoid")))
tuningsvm$best.model##
## Call:
## best.tune(METHOD = svm, train.x = Target_PJK ~ ., data = smote_reglog,
## ranges = list(kernel = c("radial", "linear", "polynomial", "sigmoid")))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 2270tune_svm <- tune(svm, Target_PJK~.,data=smote_reglog,
kernel="radial", ranges=list(cost=10^(-2:2), gamma=c(.25,.5,1,2)))
print(tune_svm)##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost gamma
## 10 1
##
## - best performance: 0.1106667modelsvmsmote <- svm(Target_PJK ~.,data=smote_reglog, kernel="radial", cost=100, gamma=1)
confusionMatrix(predict(modelsvmsmote,test_reglog), as.factor(test_reglog$Target_PJK), positive = "1")## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1316 129
## 1 140 12
##
## Accuracy : 0.8316
## 95% CI : (0.8123, 0.8496)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 1.0000
##
## Kappa : -0.0107
##
## Mcnemar's Test P-Value : 0.5421
##
## Sensitivity : 0.085106
## Specificity : 0.903846
## Pos Pred Value : 0.078947
## Neg Pred Value : 0.910727
## Prevalence : 0.088291
## Detection Rate : 0.007514
## Detection Prevalence : 0.095178
## Balanced Accuracy : 0.494476
##
## 'Positive' Class : 1
## library(e1071)
tuningsvm <- tune(svm, Target_PJK~.,data=oversampling_reglog,
ranges=list(kernel=c("radial","linear","polynomial","sigmoid")))
tuningsvm$best.model##
## Call:
## best.tune(METHOD = svm, train.x = Target_PJK ~ ., data = oversampling_reglog,
## ranges = list(kernel = c("radial", "linear", "polynomial", "sigmoid")))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 2875tune_svm <- tune(svm, Target_PJK~.,data=oversampling_reglog,
kernel="radial", ranges=list(cost=10^(-2:2), gamma=c(.25,.5,1,2)))
print(tune_svm)##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost gamma
## 1 0.25
##
## - best performance: 0.2960607modelsvmover <- svm(Target_PJK ~.,data=oversampling_reglog, kernel="radial", cost=1, gamma=.25)
confusionMatrix(predict(modelsvmover,test_reglog), as.factor(test_reglog$Target_PJK), positive = "1")## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1152 80
## 1 304 61
##
## Accuracy : 0.7595
## 95% CI : (0.7378, 0.7803)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.1303
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.43262
## Specificity : 0.79121
## Pos Pred Value : 0.16712
## Neg Pred Value : 0.93506
## Prevalence : 0.08829
## Detection Rate : 0.03820
## Detection Prevalence : 0.22855
## Balanced Accuracy : 0.61192
##
## 'Positive' Class : 1
## library(e1071)
tuningsvm <- tune(svm, Target_PJK~.,data=undersampled_reglog,
ranges=list(kernel=c("radial","linear","polynomial","sigmoid")))
tuningsvm$best.model##
## Call:
## best.tune(METHOD = svm, train.x = Target_PJK ~ ., data = undersampled_reglog,
## ranges = list(kernel = c("radial", "linear", "polynomial", "sigmoid")))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 1
##
## Number of Support Vectors: 554tune_svm <- tune(svm, Target_PJK~.,data=undersampled_reglog,
kernel="radial", ranges=list(cost=10^(-2:2), gamma=c(.25,.5,1,2)))
print(tune_svm)##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost gamma
## 1 0.25
##
## - best performance: 0.4249883modelsvmunder <- svm(Target_PJK ~.,data=undersampled_reglog, kernel="radial", cost=1, gamma=.5)
confusionMatrix(predict(modelsvmunder,test_reglog), as.factor(test_reglog$Target_PJK), positive = "1")## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 768 43
## 1 688 98
##
## Accuracy : 0.5423
## 95% CI : (0.5175, 0.5669)
## No Information Rate : 0.9117
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0726
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.69504
## Specificity : 0.52747
## Pos Pred Value : 0.12468
## Neg Pred Value : 0.94698
## Prevalence : 0.08829
## Detection Rate : 0.06137
## Detection Prevalence : 0.49217
## Balanced Accuracy : 0.61125
##
## 'Positive' Class : 1
##