## [1] 238 18
## [1] 99 18
## 2.915 sec elapsed
## Support Vector Machines with Radial Basis Function Kernel
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## C Accuracy Kappa
## 0.25 0.6643116 0.3029891
## 0.50 0.6936594 0.3676600
## 1.00 0.6934783 0.3714545
##
## Tuning parameter 'sigma' was held constant at a value of 0.0360758
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.0360758 and C = 0.5.
## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Severe Severe
## Not Severe 20 13
## Severe 45 21
##
## Accuracy : 0.4141
## 95% CI : (0.316, 0.5176)
## No Information Rate : 0.6566
## P-Value [Acc > NIR] : 1
##
## Kappa : -0.061
##
## Mcnemar's Test P-Value : 4.691e-05
##
## Sensitivity : 0.6176
## Specificity : 0.3077
## Pos Pred Value : 0.3182
## Neg Pred Value : 0.6061
## Precision : 0.3182
## Recall : 0.6176
## F1 : 0.4200
## Prevalence : 0.3434
## Detection Rate : 0.2121
## Detection Prevalence : 0.6667
## Balanced Accuracy : 0.4627
##
## 'Positive' Class : Severe
##
## Reference
## Prediction Not Severe Severe
## Not Severe 20 13
## Severe 45 21
## 7.281 sec elapsed
## Random Forest
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.8110507 0.6129572
## 9 0.8442029 0.6853067
## 17 0.8150362 0.6248898
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 9.
## Reference
## Prediction Not Severe Severe
## Not Severe 35 20
## Severe 30 14
## Generalized Linear Model
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results:
##
## Accuracy Kappa
## 0.6476449 0.283878
## Reference
## Prediction Not Severe Severe
## Not Severe 30 18
## Severe 35 16
## k-Nearest Neighbors
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## Pre-processing: centered (17), scaled (17)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.6224638 0.2144296
## 7 0.6094203 0.1863713
## 9 0.5842391 0.1370705
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 5.
## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Severe Severe
## Not Severe 25 9
## Severe 40 25
##
## Accuracy : 0.5051
## 95% CI : (0.4027, 0.6071)
## No Information Rate : 0.6566
## P-Value [Acc > NIR] : 0.9993
##
## Kappa : 0.0985
##
## Mcnemar's Test P-Value : 1.822e-05
##
## Sensitivity : 0.7353
## Specificity : 0.3846
## Pos Pred Value : 0.3846
## Neg Pred Value : 0.7353
## Precision : 0.3846
## Recall : 0.7353
## F1 : 0.5051
## Prevalence : 0.3434
## Detection Rate : 0.2525
## Detection Prevalence : 0.6566
## Balanced Accuracy : 0.5600
##
## 'Positive' Class : Severe
##
## Reference
## Prediction Not Severe Severe
## Not Severe 25 9
## Severe 40 25
## # weights: 20
## initial value 147.925402
## iter 10 value 144.747791
## iter 20 value 130.526618
## iter 30 value 116.797177
## iter 40 value 115.236872
## iter 50 value 115.194454
## iter 60 value 115.179087
## iter 70 value 115.175224
## iter 80 value 115.174359
## iter 90 value 115.171694
## iter 100 value 115.167965
## final value 115.167965
## stopped after 100 iterations
## # weights: 58
## initial value 161.558128
## iter 10 value 132.106562
## iter 20 value 93.193241
## iter 30 value 70.174258
## iter 40 value 69.191037
## iter 50 value 67.526395
## iter 60 value 67.446158
## iter 70 value 67.425121
## iter 80 value 66.867646
## iter 90 value 61.466698
## iter 100 value 56.606560
## final value 56.606560
## stopped after 100 iterations
## # weights: 96
## initial value 151.022627
## iter 10 value 145.395402
## iter 20 value 110.152797
## iter 30 value 78.989229
## iter 40 value 62.871554
## iter 50 value 60.198435
## iter 60 value 54.680596
## iter 70 value 51.709632
## iter 80 value 48.727688
## iter 90 value 48.235767
## iter 100 value 47.727082
## final value 47.727082
## stopped after 100 iterations
## # weights: 20
## initial value 148.412683
## iter 10 value 147.186926
## iter 20 value 141.309957
## iter 30 value 130.734094
## iter 40 value 122.823105
## iter 50 value 119.723497
## iter 60 value 119.398730
## iter 60 value 119.398730
## final value 119.398730
## converged
## # weights: 58
## initial value 156.146289
## iter 10 value 147.338616
## iter 20 value 127.945125
## iter 30 value 118.800812
## iter 40 value 117.739485
## iter 50 value 110.279856
## iter 60 value 100.255061
## iter 70 value 97.477424
## iter 80 value 96.529571
## iter 90 value 93.630866
## iter 100 value 87.293335
## final value 87.293335
## stopped after 100 iterations
## # weights: 96
## initial value 157.773105
## iter 10 value 146.756096
## iter 20 value 137.629117
## iter 30 value 111.420286
## iter 40 value 90.151167
## iter 50 value 81.876733
## iter 60 value 77.800942
## iter 70 value 74.357043
## iter 80 value 71.111395
## iter 90 value 70.754581
## iter 100 value 70.586295
## final value 70.586295
## stopped after 100 iterations
## # weights: 20
## initial value 148.150946
## iter 10 value 141.692144
## iter 20 value 130.983856
## iter 30 value 128.179746
## iter 40 value 128.151027
## iter 50 value 128.138772
## iter 60 value 128.127825
## iter 70 value 128.120310
## iter 80 value 128.109955
## iter 90 value 128.102111
## iter 100 value 128.100088
## final value 128.100088
## stopped after 100 iterations
## # weights: 58
## initial value 160.171970
## iter 10 value 142.902195
## iter 20 value 120.389045
## iter 30 value 109.344489
## iter 40 value 102.194319
## iter 50 value 102.016310
## iter 60 value 101.970105
## iter 70 value 101.955206
## iter 80 value 101.941514
## iter 90 value 101.935657
## iter 100 value 101.929694
## final value 101.929694
## stopped after 100 iterations
## # weights: 96
## initial value 147.903632
## iter 10 value 117.531431
## iter 20 value 89.041835
## iter 30 value 79.959148
## iter 40 value 77.138770
## iter 50 value 76.969209
## iter 60 value 76.934500
## iter 70 value 76.749538
## iter 80 value 75.490761
## iter 90 value 75.306820
## iter 100 value 75.215516
## final value 75.215516
## stopped after 100 iterations
## # weights: 20
## initial value 148.588039
## iter 10 value 147.539994
## iter 20 value 142.637716
## iter 30 value 120.295092
## iter 40 value 111.027801
## iter 50 value 110.910420
## final value 110.910150
## converged
## # weights: 58
## initial value 148.311616
## iter 10 value 144.064195
## iter 20 value 128.186046
## iter 30 value 112.460438
## iter 40 value 108.853371
## iter 50 value 108.716987
## iter 60 value 108.667291
## iter 70 value 108.642167
## iter 80 value 108.630645
## iter 90 value 108.552035
## iter 100 value 106.400647
## final value 106.400647
## stopped after 100 iterations
## # weights: 96
## initial value 147.617509
## iter 10 value 142.814788
## iter 20 value 121.096816
## iter 30 value 99.479158
## iter 40 value 65.374637
## iter 50 value 60.197587
## iter 60 value 58.813380
## iter 70 value 57.773860
## iter 80 value 55.853903
## iter 90 value 54.950858
## iter 100 value 52.281720
## final value 52.281720
## stopped after 100 iterations
## # weights: 20
## initial value 152.436094
## iter 10 value 142.624034
## iter 20 value 118.942098
## iter 30 value 117.340489
## final value 117.056185
## converged
## # weights: 58
## initial value 150.044417
## iter 10 value 145.989551
## iter 20 value 133.216775
## iter 30 value 118.650382
## iter 40 value 107.110749
## iter 50 value 105.077064
## iter 60 value 104.128390
## iter 70 value 104.017018
## iter 80 value 103.955645
## iter 90 value 101.558194
## iter 100 value 99.943789
## final value 99.943789
## stopped after 100 iterations
## # weights: 96
## initial value 174.170154
## iter 10 value 141.434704
## iter 20 value 124.740177
## iter 30 value 110.008429
## iter 40 value 93.993880
## iter 50 value 87.607055
## iter 60 value 83.517165
## iter 70 value 79.543385
## iter 80 value 77.751268
## iter 90 value 76.525811
## iter 100 value 76.121663
## final value 76.121663
## stopped after 100 iterations
## # weights: 20
## initial value 152.930653
## iter 10 value 146.406434
## iter 20 value 133.618418
## iter 30 value 131.748494
## iter 40 value 129.942009
## iter 50 value 129.929972
## iter 60 value 129.854001
## iter 70 value 128.182166
## iter 80 value 128.083847
## iter 90 value 127.270024
## iter 100 value 127.137701
## final value 127.137701
## stopped after 100 iterations
## # weights: 58
## initial value 149.334977
## iter 10 value 135.761810
## iter 20 value 119.498705
## iter 30 value 109.981577
## iter 40 value 107.572807
## iter 50 value 106.629776
## iter 60 value 105.691832
## iter 70 value 105.625602
## iter 80 value 105.593737
## iter 90 value 105.576300
## iter 100 value 105.547871
## final value 105.547871
## stopped after 100 iterations
## # weights: 96
## initial value 147.805857
## iter 10 value 142.183722
## iter 20 value 118.056128
## iter 30 value 112.838034
## iter 40 value 111.697133
## iter 50 value 111.605013
## iter 60 value 111.565742
## iter 70 value 111.496047
## iter 80 value 111.394056
## iter 90 value 111.360651
## iter 100 value 111.327524
## final value 111.327524
## stopped after 100 iterations
## # weights: 20
## initial value 154.398343
## iter 10 value 147.546551
## iter 20 value 126.506269
## iter 30 value 110.313487
## iter 40 value 108.429044
## iter 50 value 107.275853
## iter 60 value 107.261338
## final value 107.261083
## converged
## # weights: 58
## initial value 175.779517
## iter 10 value 133.992774
## iter 20 value 108.079325
## iter 30 value 85.917665
## iter 40 value 78.651019
## iter 50 value 76.638436
## iter 60 value 76.379405
## iter 70 value 76.060335
## iter 80 value 75.859469
## iter 90 value 75.788908
## iter 100 value 75.706906
## final value 75.706906
## stopped after 100 iterations
## # weights: 96
## initial value 159.653880
## iter 10 value 133.016137
## iter 20 value 108.393370
## iter 30 value 92.659302
## iter 40 value 67.226876
## iter 50 value 63.571440
## iter 60 value 63.424414
## iter 70 value 63.422607
## iter 80 value 63.422154
## iter 90 value 63.422028
## iter 100 value 63.421757
## final value 63.421757
## stopped after 100 iterations
## # weights: 20
## initial value 160.413170
## iter 10 value 128.882498
## iter 20 value 121.235140
## iter 30 value 121.139186
## final value 121.139144
## converged
## # weights: 58
## initial value 148.507868
## iter 10 value 123.877040
## iter 20 value 115.352441
## iter 30 value 101.045075
## iter 40 value 97.498748
## iter 50 value 96.547806
## iter 60 value 95.397052
## iter 70 value 93.311862
## iter 80 value 92.839157
## iter 90 value 92.808909
## final value 92.808865
## converged
## # weights: 96
## initial value 149.643074
## iter 10 value 146.232729
## iter 20 value 136.941652
## iter 30 value 118.886906
## iter 40 value 112.772077
## iter 50 value 103.454948
## iter 60 value 95.110533
## iter 70 value 92.196125
## iter 80 value 90.923414
## iter 90 value 90.200502
## iter 100 value 85.949781
## final value 85.949781
## stopped after 100 iterations
## # weights: 20
## initial value 161.549200
## iter 10 value 147.505094
## iter 20 value 144.747313
## iter 30 value 127.699408
## iter 40 value 120.650574
## iter 50 value 120.454929
## iter 60 value 120.377741
## iter 70 value 120.371244
## iter 80 value 120.370346
## iter 90 value 120.370012
## iter 100 value 120.369658
## final value 120.369658
## stopped after 100 iterations
## # weights: 58
## initial value 160.494377
## iter 10 value 146.840620
## iter 20 value 140.217195
## iter 30 value 103.720452
## iter 40 value 96.142134
## iter 50 value 96.080391
## iter 60 value 96.068137
## iter 70 value 96.062964
## iter 80 value 96.049468
## iter 90 value 96.042031
## iter 100 value 96.037956
## final value 96.037956
## stopped after 100 iterations
## # weights: 96
## initial value 160.476124
## iter 10 value 130.760414
## iter 20 value 115.940224
## iter 30 value 111.528771
## iter 40 value 107.583240
## iter 50 value 104.732443
## iter 60 value 102.749455
## iter 70 value 85.706332
## iter 80 value 81.398768
## iter 90 value 55.766369
## iter 100 value 42.867194
## final value 42.867194
## stopped after 100 iterations
## # weights: 20
## initial value 200.506555
## final value 147.575718
## converged
## # weights: 58
## initial value 150.447123
## iter 10 value 141.030377
## iter 20 value 123.414201
## iter 30 value 103.542847
## iter 40 value 92.972522
## iter 50 value 90.552943
## iter 60 value 89.069709
## iter 70 value 88.813902
## iter 80 value 88.806532
## iter 90 value 88.803771
## iter 100 value 88.803140
## final value 88.803140
## stopped after 100 iterations
## # weights: 96
## initial value 148.439539
## iter 10 value 135.235023
## iter 20 value 108.313079
## iter 30 value 93.248840
## iter 40 value 91.056734
## iter 50 value 86.430913
## iter 60 value 85.615864
## iter 70 value 83.842053
## iter 80 value 83.311628
## iter 90 value 82.513955
## iter 100 value 82.338107
## final value 82.338107
## stopped after 100 iterations
## # weights: 20
## initial value 153.791696
## iter 10 value 139.953513
## iter 20 value 128.427599
## iter 30 value 120.146708
## iter 40 value 119.905728
## final value 119.898461
## converged
## # weights: 58
## initial value 167.311716
## iter 10 value 146.562554
## iter 20 value 122.686380
## iter 30 value 115.324416
## iter 40 value 105.852764
## iter 50 value 100.906870
## iter 60 value 89.844266
## iter 70 value 87.218296
## iter 80 value 86.491551
## iter 90 value 86.446860
## iter 100 value 86.446268
## final value 86.446268
## stopped after 100 iterations
## # weights: 96
## initial value 153.062037
## iter 10 value 147.054752
## iter 20 value 137.126179
## iter 30 value 121.120936
## iter 40 value 106.904353
## iter 50 value 102.423499
## iter 60 value 99.489186
## iter 70 value 94.796317
## iter 80 value 93.417866
## iter 90 value 90.476442
## iter 100 value 88.786362
## final value 88.786362
## stopped after 100 iterations
## # weights: 20
## initial value 154.571887
## iter 10 value 147.575694
## iter 20 value 147.571293
## iter 30 value 129.882127
## iter 40 value 112.489584
## iter 50 value 103.059087
## iter 60 value 102.133182
## iter 70 value 102.044984
## iter 80 value 101.324496
## iter 90 value 101.185797
## iter 100 value 101.069154
## final value 101.069154
## stopped after 100 iterations
## # weights: 58
## initial value 162.693642
## iter 10 value 143.651786
## iter 20 value 124.330047
## iter 30 value 115.302015
## iter 40 value 110.658272
## iter 50 value 108.133628
## iter 60 value 103.898548
## iter 70 value 101.837738
## iter 80 value 100.551055
## iter 90 value 100.412171
## iter 100 value 100.393522
## final value 100.393522
## stopped after 100 iterations
## # weights: 96
## initial value 158.025499
## iter 10 value 146.127805
## iter 20 value 123.090986
## iter 30 value 108.014015
## iter 40 value 106.452707
## iter 50 value 70.882952
## iter 60 value 56.366420
## iter 70 value 52.951543
## iter 80 value 52.688775
## iter 90 value 52.424317
## iter 100 value 52.198332
## final value 52.198332
## stopped after 100 iterations
## # weights: 20
## initial value 148.634210
## iter 10 value 147.574227
## iter 20 value 143.779021
## iter 30 value 118.544728
## iter 40 value 107.585175
## iter 50 value 107.229873
## final value 107.229463
## converged
## # weights: 58
## initial value 147.936827
## iter 10 value 147.468041
## iter 20 value 121.344958
## iter 30 value 107.990724
## iter 40 value 97.161426
## iter 50 value 96.916632
## iter 60 value 87.940507
## iter 70 value 84.682131
## iter 80 value 82.921825
## iter 90 value 80.480741
## iter 100 value 79.958167
## final value 79.958167
## stopped after 100 iterations
## # weights: 96
## initial value 168.355738
## iter 10 value 146.807644
## iter 20 value 140.164053
## iter 30 value 106.770129
## iter 40 value 93.628944
## iter 50 value 90.907888
## iter 60 value 89.997974
## iter 70 value 89.824054
## iter 80 value 89.557742
## iter 90 value 87.221826
## iter 100 value 86.812172
## final value 86.812172
## stopped after 100 iterations
## # weights: 20
## initial value 149.588836
## iter 10 value 147.461662
## iter 20 value 134.153395
## iter 30 value 125.301196
## iter 40 value 121.045330
## iter 50 value 120.991839
## final value 120.991831
## converged
## # weights: 58
## initial value 173.808620
## iter 10 value 141.813511
## iter 20 value 124.111911
## iter 30 value 123.275579
## iter 40 value 120.603042
## iter 50 value 114.321670
## iter 60 value 104.399456
## iter 70 value 95.055347
## iter 80 value 91.469440
## iter 90 value 90.097409
## iter 100 value 89.778573
## final value 89.778573
## stopped after 100 iterations
## # weights: 96
## initial value 155.624033
## iter 10 value 138.785256
## iter 20 value 119.681661
## iter 30 value 107.671621
## iter 40 value 89.916299
## iter 50 value 85.575683
## iter 60 value 83.393050
## iter 70 value 82.102427
## iter 80 value 81.400283
## iter 90 value 80.692386
## iter 100 value 80.494177
## final value 80.494177
## stopped after 100 iterations
## # weights: 20
## initial value 164.548069
## iter 10 value 133.373475
## iter 20 value 130.182488
## iter 30 value 128.067957
## iter 40 value 124.682148
## iter 50 value 124.368330
## iter 60 value 122.085803
## iter 70 value 121.690502
## iter 80 value 121.655304
## iter 90 value 121.619160
## iter 100 value 120.619032
## final value 120.619032
## stopped after 100 iterations
## # weights: 58
## initial value 159.354839
## iter 10 value 142.662998
## iter 20 value 110.675647
## iter 30 value 100.713771
## iter 40 value 100.551734
## iter 50 value 100.484485
## iter 60 value 99.614113
## iter 70 value 95.936219
## iter 80 value 94.774463
## iter 90 value 93.058661
## iter 100 value 89.745859
## final value 89.745859
## stopped after 100 iterations
## # weights: 96
## initial value 198.075397
## iter 10 value 146.592684
## iter 20 value 122.980164
## iter 30 value 115.240902
## iter 40 value 89.081224
## iter 50 value 72.717078
## iter 60 value 63.877096
## iter 70 value 60.711260
## iter 80 value 58.028212
## iter 90 value 57.186268
## iter 100 value 56.556384
## final value 56.556384
## stopped after 100 iterations
## # weights: 20
## initial value 166.297609
## iter 10 value 147.341840
## iter 20 value 131.999324
## iter 30 value 121.624227
## iter 40 value 121.206132
## final value 121.205193
## converged
## # weights: 58
## initial value 247.621420
## iter 10 value 145.616804
## iter 20 value 115.207886
## iter 30 value 106.576480
## iter 40 value 104.711144
## iter 50 value 103.626930
## iter 60 value 93.487358
## iter 70 value 79.749601
## iter 80 value 77.760681
## iter 90 value 76.710997
## iter 100 value 69.464307
## final value 69.464307
## stopped after 100 iterations
## # weights: 96
## initial value 181.162325
## iter 10 value 129.330494
## iter 20 value 107.253054
## iter 30 value 103.754497
## iter 40 value 103.718505
## final value 103.718397
## converged
## # weights: 20
## initial value 148.884046
## iter 10 value 143.588625
## iter 20 value 123.282364
## iter 30 value 120.530854
## iter 40 value 120.481454
## final value 120.480051
## converged
## # weights: 58
## initial value 157.647919
## iter 10 value 146.825804
## iter 20 value 132.195774
## iter 30 value 118.471033
## iter 40 value 110.989069
## iter 50 value 107.690112
## iter 60 value 103.423374
## iter 70 value 102.237687
## iter 80 value 102.086024
## iter 90 value 102.077133
## final value 102.077117
## converged
## # weights: 96
## initial value 150.241132
## iter 10 value 146.767471
## iter 20 value 116.793672
## iter 30 value 109.465777
## iter 40 value 104.952417
## iter 50 value 95.280378
## iter 60 value 88.073196
## iter 70 value 85.935561
## iter 80 value 85.013822
## iter 90 value 82.164071
## iter 100 value 81.360850
## final value 81.360850
## stopped after 100 iterations
## # weights: 20
## initial value 148.138400
## iter 10 value 139.503407
## iter 20 value 120.948666
## iter 30 value 106.368079
## iter 40 value 104.521842
## iter 50 value 104.431181
## iter 60 value 104.400197
## iter 70 value 104.392158
## iter 80 value 104.391446
## iter 90 value 104.391272
## final value 104.391255
## converged
## # weights: 58
## initial value 150.660616
## iter 10 value 145.315293
## iter 20 value 104.186013
## iter 30 value 84.279324
## iter 40 value 69.617592
## iter 50 value 64.691839
## iter 60 value 64.135053
## iter 70 value 64.038704
## iter 80 value 63.996804
## iter 90 value 63.932335
## iter 100 value 63.901579
## final value 63.901579
## stopped after 100 iterations
## # weights: 96
## initial value 157.706518
## iter 10 value 144.806122
## iter 20 value 124.683179
## iter 30 value 99.280605
## iter 40 value 66.467157
## iter 50 value 60.433378
## iter 60 value 53.567529
## iter 70 value 50.093138
## iter 80 value 49.560964
## iter 90 value 47.779519
## iter 100 value 46.930357
## final value 46.930357
## stopped after 100 iterations
## # weights: 20
## initial value 166.401926
## iter 10 value 146.943149
## iter 20 value 127.846789
## iter 30 value 125.170935
## iter 40 value 125.107813
## iter 50 value 124.580284
## iter 60 value 122.225328
## iter 70 value 122.199491
## iter 80 value 122.198025
## final value 122.197721
## converged
## # weights: 58
## initial value 159.089028
## iter 10 value 143.558040
## iter 20 value 123.699649
## iter 30 value 118.664970
## iter 40 value 116.838428
## iter 50 value 114.566072
## iter 60 value 113.525343
## iter 70 value 113.126347
## iter 80 value 111.918892
## iter 90 value 111.402370
## iter 100 value 111.367267
## final value 111.367267
## stopped after 100 iterations
## # weights: 96
## initial value 168.866574
## iter 10 value 129.496076
## iter 20 value 114.659292
## iter 30 value 101.063660
## iter 40 value 100.856723
## iter 50 value 100.856128
## iter 50 value 100.856128
## iter 50 value 100.856128
## final value 100.856128
## converged
## # weights: 20
## initial value 148.402775
## iter 10 value 146.170620
## iter 20 value 129.842281
## iter 30 value 121.824255
## iter 40 value 121.742218
## final value 121.741991
## converged
## # weights: 58
## initial value 152.286882
## iter 10 value 145.239227
## iter 20 value 124.901153
## iter 30 value 114.808510
## iter 40 value 106.074244
## iter 50 value 100.996599
## iter 60 value 100.596429
## iter 70 value 100.573764
## iter 80 value 100.573325
## final value 100.573324
## converged
## # weights: 96
## initial value 230.100073
## iter 10 value 153.327712
## iter 20 value 132.236637
## iter 30 value 115.187279
## iter 40 value 105.701008
## iter 50 value 101.918375
## iter 60 value 97.913367
## iter 70 value 93.210939
## iter 80 value 90.659628
## iter 90 value 86.625061
## iter 100 value 84.408972
## final value 84.408972
## stopped after 100 iterations
## # weights: 20
## initial value 148.638710
## iter 10 value 137.906808
## iter 20 value 131.809048
## iter 30 value 129.812130
## iter 40 value 128.058545
## iter 50 value 128.038324
## iter 60 value 128.032339
## iter 70 value 128.030561
## iter 80 value 128.029068
## iter 90 value 127.183740
## iter 100 value 127.070193
## final value 127.070193
## stopped after 100 iterations
## # weights: 58
## initial value 169.022102
## iter 10 value 147.544987
## iter 20 value 146.104287
## iter 30 value 115.625102
## iter 40 value 112.287629
## iter 50 value 111.856919
## iter 60 value 105.209883
## iter 70 value 102.449551
## iter 80 value 102.344489
## iter 90 value 102.316960
## iter 100 value 102.288724
## final value 102.288724
## stopped after 100 iterations
## # weights: 96
## initial value 180.762545
## iter 10 value 146.274907
## iter 20 value 115.880218
## iter 30 value 92.531011
## iter 40 value 71.760665
## iter 50 value 66.816258
## iter 60 value 65.462135
## iter 70 value 64.424135
## iter 80 value 64.255826
## iter 90 value 64.192294
## iter 100 value 64.169018
## final value 64.169018
## stopped after 100 iterations
## # weights: 20
## initial value 173.363221
## iter 10 value 146.971198
## iter 20 value 124.924826
## iter 30 value 114.560916
## iter 40 value 110.034939
## iter 50 value 109.861362
## final value 109.861103
## converged
## # weights: 58
## initial value 158.615314
## iter 10 value 144.652441
## iter 20 value 126.076840
## iter 30 value 125.971538
## iter 40 value 125.969397
## iter 40 value 125.969396
## iter 40 value 125.969396
## final value 125.969396
## converged
## # weights: 96
## initial value 149.699696
## iter 10 value 133.472086
## iter 20 value 103.158325
## iter 30 value 86.275179
## iter 40 value 85.941223
## iter 50 value 85.934185
## final value 85.934126
## converged
## # weights: 20
## initial value 154.534676
## iter 10 value 148.160231
## iter 20 value 133.466690
## iter 30 value 124.270232
## iter 40 value 123.358166
## iter 50 value 123.054554
## iter 60 value 120.798316
## iter 70 value 120.673561
## final value 120.673530
## converged
## # weights: 58
## initial value 175.458657
## iter 10 value 143.546646
## iter 20 value 126.405698
## iter 30 value 119.186633
## iter 40 value 114.985910
## iter 50 value 110.132985
## iter 60 value 106.535307
## iter 70 value 103.092256
## iter 80 value 100.786617
## iter 90 value 100.747563
## final value 100.746111
## converged
## # weights: 96
## initial value 162.030119
## iter 10 value 143.868487
## iter 20 value 125.734485
## iter 30 value 111.984485
## iter 40 value 104.701975
## iter 50 value 94.772186
## iter 60 value 92.946008
## iter 70 value 91.301056
## iter 80 value 89.377624
## iter 90 value 88.343347
## iter 100 value 85.492554
## final value 85.492554
## stopped after 100 iterations
## # weights: 20
## initial value 148.196209
## iter 10 value 145.372787
## iter 20 value 122.966017
## iter 30 value 117.443771
## iter 40 value 117.304627
## iter 50 value 117.262550
## iter 60 value 117.252877
## iter 70 value 117.224465
## iter 80 value 117.214864
## iter 90 value 117.211460
## iter 100 value 117.209226
## final value 117.209226
## stopped after 100 iterations
## # weights: 58
## initial value 148.051239
## iter 10 value 142.217465
## iter 20 value 124.356832
## iter 30 value 121.807088
## iter 40 value 120.995755
## iter 50 value 120.328356
## iter 60 value 120.091159
## iter 70 value 119.764328
## iter 80 value 119.481767
## iter 90 value 119.290865
## iter 100 value 119.244869
## final value 119.244869
## stopped after 100 iterations
## # weights: 96
## initial value 152.633203
## iter 10 value 146.315591
## iter 20 value 120.398397
## iter 30 value 112.186445
## iter 40 value 99.947715
## iter 50 value 98.399458
## iter 60 value 96.432879
## iter 70 value 94.587250
## iter 80 value 93.801402
## iter 90 value 92.692678
## iter 100 value 92.536793
## final value 92.536793
## stopped after 100 iterations
## # weights: 20
## initial value 151.384414
## iter 10 value 147.989493
## iter 20 value 143.866759
## iter 30 value 139.306669
## iter 40 value 139.300202
## final value 139.300194
## converged
## # weights: 58
## initial value 152.747355
## iter 10 value 148.104966
## iter 20 value 129.990740
## iter 30 value 110.694974
## iter 40 value 79.284846
## iter 50 value 73.034796
## iter 60 value 72.558417
## iter 70 value 72.553119
## final value 72.553089
## converged
## # weights: 96
## initial value 151.891941
## iter 10 value 140.037205
## iter 20 value 114.945313
## iter 30 value 100.691256
## iter 40 value 86.908762
## iter 50 value 82.301760
## iter 60 value 81.852086
## iter 70 value 81.734990
## iter 80 value 81.669447
## iter 90 value 81.650502
## iter 100 value 81.647839
## final value 81.647839
## stopped after 100 iterations
## # weights: 20
## initial value 148.952542
## iter 10 value 146.600514
## iter 20 value 125.319390
## iter 30 value 122.599206
## iter 40 value 122.519167
## final value 122.518917
## converged
## # weights: 58
## initial value 149.399272
## iter 10 value 146.726845
## iter 20 value 124.398676
## iter 30 value 120.748529
## iter 40 value 114.380969
## iter 50 value 106.236082
## iter 60 value 104.863403
## iter 70 value 102.999360
## iter 80 value 96.508850
## iter 90 value 90.182535
## iter 100 value 89.494439
## final value 89.494439
## stopped after 100 iterations
## # weights: 96
## initial value 153.243039
## iter 10 value 140.094952
## iter 20 value 118.899079
## iter 30 value 107.597250
## iter 40 value 96.918169
## iter 50 value 88.993601
## iter 60 value 86.683271
## iter 70 value 83.841193
## iter 80 value 83.233967
## iter 90 value 82.996106
## iter 100 value 82.903493
## final value 82.903493
## stopped after 100 iterations
## # weights: 20
## initial value 148.484989
## iter 10 value 148.166004
## iter 20 value 140.256009
## iter 30 value 134.525358
## iter 40 value 134.472862
## iter 50 value 134.437962
## iter 60 value 134.378262
## iter 70 value 134.367524
## iter 80 value 134.356995
## iter 90 value 134.348922
## iter 100 value 134.345459
## final value 134.345459
## stopped after 100 iterations
## # weights: 58
## initial value 148.078018
## iter 10 value 127.533470
## iter 20 value 116.925598
## iter 30 value 113.528922
## iter 40 value 94.005907
## iter 50 value 90.423340
## iter 60 value 90.042782
## iter 70 value 89.974430
## iter 80 value 89.939815
## iter 90 value 89.917653
## iter 100 value 89.894952
## final value 89.894952
## stopped after 100 iterations
## # weights: 96
## initial value 163.850587
## iter 10 value 144.097772
## iter 20 value 116.758964
## iter 30 value 101.109016
## iter 40 value 94.559731
## iter 50 value 91.768225
## iter 60 value 91.391156
## iter 70 value 89.875312
## iter 80 value 85.743514
## iter 90 value 85.696302
## iter 100 value 85.649541
## final value 85.649541
## stopped after 100 iterations
## # weights: 20
## initial value 161.831568
## iter 10 value 147.573968
## iter 20 value 147.078204
## iter 30 value 146.962500
## final value 146.961225
## converged
## # weights: 58
## initial value 171.233130
## iter 10 value 146.865608
## iter 20 value 119.882347
## iter 30 value 106.016963
## iter 40 value 99.403368
## iter 50 value 99.219641
## iter 60 value 99.218250
## iter 60 value 99.218249
## iter 60 value 99.218249
## final value 99.218249
## converged
## # weights: 96
## initial value 149.656694
## iter 10 value 133.537327
## iter 20 value 100.238717
## iter 30 value 83.923091
## iter 40 value 81.155773
## iter 50 value 70.691007
## iter 60 value 47.454652
## iter 70 value 41.724397
## iter 80 value 39.368206
## iter 90 value 38.863255
## iter 100 value 38.626162
## final value 38.626162
## stopped after 100 iterations
## # weights: 20
## initial value 151.044679
## iter 10 value 147.238669
## iter 20 value 131.515476
## iter 30 value 125.377201
## iter 40 value 125.134439
## iter 50 value 123.092044
## iter 60 value 121.533767
## iter 70 value 121.395359
## final value 121.395311
## converged
## # weights: 58
## initial value 158.501498
## iter 10 value 136.977810
## iter 20 value 126.772869
## iter 30 value 119.860507
## iter 40 value 107.340442
## iter 50 value 97.921007
## iter 60 value 93.680166
## iter 70 value 93.416705
## iter 80 value 91.330912
## iter 90 value 90.673254
## iter 100 value 90.640914
## final value 90.640914
## stopped after 100 iterations
## # weights: 96
## initial value 172.017641
## iter 10 value 147.642398
## iter 20 value 128.985473
## iter 30 value 122.573420
## iter 40 value 121.917965
## iter 50 value 121.869797
## iter 60 value 121.842900
## iter 70 value 121.826656
## iter 80 value 121.823042
## final value 121.813844
## converged
## # weights: 20
## initial value 149.455304
## iter 10 value 146.108614
## iter 20 value 131.902488
## iter 30 value 131.848929
## iter 40 value 131.822516
## iter 50 value 131.798745
## iter 60 value 131.128488
## iter 70 value 130.883609
## iter 80 value 130.873793
## iter 90 value 130.862991
## iter 100 value 130.854131
## final value 130.854131
## stopped after 100 iterations
## # weights: 58
## initial value 167.723169
## iter 10 value 118.144890
## iter 20 value 103.082328
## iter 30 value 92.108869
## iter 40 value 91.302317
## iter 50 value 91.102901
## iter 60 value 90.780684
## iter 70 value 89.840832
## iter 80 value 89.586843
## iter 90 value 88.239951
## iter 100 value 88.162224
## final value 88.162224
## stopped after 100 iterations
## # weights: 96
## initial value 150.454406
## iter 10 value 146.109124
## iter 20 value 118.399476
## iter 30 value 83.468732
## iter 40 value 56.903426
## iter 50 value 55.396508
## iter 60 value 55.201468
## iter 70 value 52.921709
## iter 80 value 51.785508
## iter 90 value 49.750653
## iter 100 value 49.545582
## final value 49.545582
## stopped after 100 iterations
## # weights: 96
## initial value 266.647305
## iter 10 value 163.853962
## iter 20 value 142.365971
## iter 30 value 133.095870
## iter 40 value 120.637698
## iter 50 value 111.371698
## iter 60 value 103.166904
## iter 70 value 100.223073
## iter 80 value 98.727887
## iter 90 value 96.324843
## iter 100 value 94.698427
## final value 94.698427
## stopped after 100 iterations
## Neural Network
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 1 0e+00 0.6086957 0.1712576
## 1 1e-04 0.6391304 0.2347447
## 1 1e-01 0.6472826 0.2662667
## 3 0e+00 0.6800725 0.3388101
## 3 1e-04 0.7217391 0.4326672
## 3 1e-01 0.7052536 0.4060518
## 5 0e+00 0.7099638 0.4013201
## 5 1e-04 0.7309783 0.4472998
## 5 1e-01 0.7601449 0.5082464
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 5 and decay = 0.1.
## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Severe Severe
## Not Severe 32 16
## Severe 33 18
##
## Accuracy : 0.5051
## 95% CI : (0.4027, 0.6071)
## No Information Rate : 0.6566
## P-Value [Acc > NIR] : 0.99932
##
## Kappa : 0.0194
##
## Mcnemar's Test P-Value : 0.02227
##
## Sensitivity : 0.5294
## Specificity : 0.4923
## Pos Pred Value : 0.3529
## Neg Pred Value : 0.6667
## Precision : 0.3529
## Recall : 0.5294
## F1 : 0.4235
## Prevalence : 0.3434
## Detection Rate : 0.1818
## Detection Prevalence : 0.5152
## Balanced Accuracy : 0.5109
##
## 'Positive' Class : Severe
##
## Reference
## Prediction Not Severe Severe
## Not Severe 32 16
## Severe 33 18
## Naive Bayes
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.6266304 0.2398071
## TRUE 0.5887681 0.1099602
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
## = 1.
## Reference
## Prediction Not Severe Severe
## Not Severe 20 12
## Severe 45 22
## Linear Discriminant Analysis
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## Pre-processing: centered (17), scaled (17)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results:
##
## Accuracy Kappa
## 0.6476449 0.283878
## Reference
## Prediction Not Severe Severe
## Not Severe 30 18
## Severe 35 16
## [1] "age" "sex" "fever" "cold"
## [5] "rigor" "fatigue" "headace" "bitter_tongue"
## [9] "vomitting" "diarrhea" "Convulsion" "Anemia"
## [13] "jundice" "cocacola_urine" "hypoglycemia" "prostraction"
## [17] "hyperpyrexia" "severe_maleria"
## age sex fever cold rigor fatigue headace bitter_tongue vomitting diarrhea
## 1 70 1 1 1 1 0 0 0 0 0
## 2 64 1 1 1 1 0 0 1 0 1
## 3 31 0 1 1 0 0 1 1 0 0
## 4 28 0 0 1 0 0 1 1 0 1
## 5 29 0 1 1 1 1 1 1 1 0
## 6 16 0 0 1 0 0 0 1 0 0
## 7 16 0 0 0 0 1 0 1 0 0
## 8 35 1 0 1 1 0 0 1 0 0
## 9 24 0 1 1 0 0 1 0 0 1
## 10 77 1 1 1 1 0 1 0 0 1
## 11 31 1 0 1 1 0 1 0 1 0
## 12 42 1 1 1 0 0 1 0 0 0
## 13 18 0 1 0 0 0 1 0 0 0
## 14 22 0 1 0 0 0 1 0 0 1
## 15 32 0 1 0 1 0 1 1 0 0
## 16 70 0 1 0 1 0 1 1 0 0
## 17 11 0 1 0 1 0 0 1 0 1
## 18 28 0 0 1 0 0 1 1 0 1
## 19 17 1 1 1 0 0 1 1 0 0
## 20 43 0 0 1 0 0 0 0 1 0
## 21 35 0 1 0 1 1 1 0 0 0
## 22 52 1 1 0 1 1 1 0 0 0
## 23 16 0 0 1 0 0 0 1 0 0
## 24 39 0 1 1 0 1 1 0 0 0
## 25 14 1 0 0 0 0 1 1 0 0
## 26 35 1 1 1 1 0 1 0 0 0
## 27 28 1 1 1 0 0 1 0 0 0
## 28 14 1 0 1 0 0 1 1 0 0
## 29 55 0 1 1 0 0 1 1 0 0
## 30 52 1 1 1 0 1 1 1 0 1
## 31 25 0 1 1 1 1 0 0 0 0
## 32 17 1 1 0 0 0 0 0 0 1
## 33 21 1 1 1 0 1 1 1 0 0
## 34 39 1 0 0 0 1 1 0 0 1
## 35 28 0 0 1 0 0 1 1 0 1
## 36 24 0 1 1 0 1 1 1 0 0
## 37 43 1 1 0 0 1 0 0 1 0
## 38 14 1 0 1 0 0 1 1 0 0
## 39 28 1 0 1 0 0 0 0 0 0
## 40 71 0 1 1 0 1 0 1 1 0
## 41 18 0 0 0 0 1 0 0 0 0
## 42 35 1 1 1 1 0 1 0 0 0
## 43 30 1 0 1 0 1 1 0 0 0
## 44 28 0 0 1 0 0 1 1 0 1
## 45 43 0 0 1 0 0 0 0 1 0
## 46 30 1 1 1 0 1 1 0 0 0
## 47 31 1 1 0 1 1 1 0 1 0
## 48 39 1 0 0 0 1 1 0 0 1
## 49 24 1 1 0 0 1 1 1 0 1
## 50 35 0 0 1 0 0 1 1 0 0
## 51 31 0 0 0 0 0 1 1 0 0
## 52 52 1 1 1 0 1 1 1 0 1
## 53 28 1 1 1 0 0 1 0 0 0
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## 55 58 1 1 1 0 0 1 0 0 0
## 56 19 0 1 0 0 0 0 0 0 0
## 57 17 0 1 1 0 0 1 0 0 1
## 58 31 1 1 1 1 0 0 0 0 1
## 59 33 1 1 0 1 0 0 1 0 0
## 60 17 1 1 0 0 0 0 0 0 1
## 61 18 1 1 0 1 1 1 1 0 0
## 62 30 1 1 1 0 1 1 0 0 0
## 63 17 1 1 0 0 0 0 0 0 1
## 64 51 1 1 1 0 1 1 1 0 0
## 65 31 0 1 1 0 0 1 0 1 0
## 66 16 1 1 1 0 1 1 0 0 1
## 67 21 1 1 1 0 1 1 1 0 0
## 68 42 1 1 1 0 1 0 1 0 0
## 69 5 1 1 0 1 1 0 0 1 1
## 70 4 1 1 1 0 1 0 0 0 0
## 71 23 1 1 0 0 1 1 0 0 1
## 72 17 1 1 1 0 0 1 1 0 0
## 73 30 1 1 1 0 0 1 1 0 0
## 74 24 0 1 1 0 1 1 1 0 0
## 75 52 0 1 1 0 0 0 0 0 0
## 76 42 1 1 1 0 1 0 1 0 0
## 77 18 1 1 0 1 1 1 1 0 0
## 78 38 0 1 0 0 1 0 1 0 0
## 79 37 0 1 1 1 0 0 0 0 1
## 80 28 1 0 1 0 0 0 0 0 0
## 81 18 0 0 0 0 1 0 0 0 0
## 82 43 1 1 0 0 1 0 0 1 0
## 83 17 0 1 1 1 1 0 0 0 0
## 84 71 0 1 0 0 0 1 0 0 1
## 85 31 0 0 1 1 0 1 0 0 0
## 86 35 1 0 1 1 0 0 1 0 0
## 87 31 0 0 0 1 0 1 1 0 0
## 88 4 0 1 1 1 0 1 0 0 0
## 89 51 1 1 1 0 1 1 1 0 0
## 90 15 1 1 0 1 1 1 0 0 0
## 91 56 0 0 1 0 0 0 0 0 0
## 92 10 1 1 0 1 0 0 1 0 0
## 93 58 1 1 1 0 0 1 0 0 0
## 94 32 1 0 1 0 0 0 0 0 1
## 95 40 1 1 0 0 0 1 0 0 1
## 96 19 0 1 0 0 0 0 0 0 0
## 97 30 1 0 0 0 0 1 1 0 1
## 98 52 1 1 0 0 0 0 0 0 0
## 99 17 0 1 1 1 1 0 0 0 0
## 100 11 0 1 0 1 0 0 1 0 1
## 101 38 1 1 1 1 0 1 0 1 0
## 102 52 1 1 0 0 0 0 0 0 0
## 103 42 1 1 1 0 1 0 1 0 0
## 104 8 0 1 0 1 1 1 0 0 0
## 105 15 0 1 0 0 0 1 1 0 0
## 106 17 1 1 1 0 0 1 1 0 0
## 107 28 1 1 0 0 1 0 0 0 0
## 108 74 1 1 0 0 0 0 0 0 0
## 109 42 0 1 1 0 1 1 0 0 0
## 110 16 1 1 0 0 1 0 0 0 0
## 111 43 0 0 0 0 1 1 0 0 1
## 112 53 1 1 1 1 1 1 1 0 1
## 113 33 0 1 1 0 1 1 1 0 0
## 114 35 1 1 0 0 1 1 1 0 0
## 115 38 0 0 1 1 0 1 0 0 0
## 116 18 1 1 0 0 1 0 1 0 1
## 117 27 1 0 0 0 0 0 0 0 0
## 118 27 1 1 1 1 1 1 0 0 0
## 119 28 0 0 0 1 0 1 0 0 0
## 120 21 0 1 1 0 0 0 0 0 0
## 121 31 1 0 0 0 1 1 0 0 0
## 122 31 0 1 1 1 0 1 0 0 1
## 123 24 1 0 1 0 0 0 0 0 1
## 124 39 1 1 0 0 0 1 0 0 1
## 125 53 1 1 1 1 1 1 1 0 1
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## 127 31 1 1 1 0 1 1 1 0 0
## 128 21 0 1 1 1 1 1 1 1 1
## 129 15 0 0 1 0 1 1 1 1 0
## 130 59 0 1 0 0 1 1 0 0 0
## 131 18 0 0 0 0 0 1 0 0 0
## 132 35 0 1 0 1 0 1 1 0 0
## 133 39 1 1 0 0 0 1 0 0 1
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## 136 25 0 1 0 0 1 0 0 0 0
## 137 43 0 0 0 0 1 1 0 0 1
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## 146 33 0 1 1 0 1 1 1 0 0
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## 150 43 1 0 0 1 1 1 1 0 1
## 151 41 1 1 0 0 1 1 0 0 0
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## 168 25 1 1 0 1 1 1 1 0 0
## 169 30 1 1 1 1 0 1 0 0 1
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## 171 24 0 1 1 1 1 1 0 0 1
## 172 26 0 1 0 0 1 1 0 0 0
## 173 42 1 1 1 0 1 1 1 0 1
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## 175 41 1 0 0 0 0 1 1 0 0
## 176 52 0 1 0 1 0 0 1 0 0
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## 181 38 0 0 1 1 0 1 0 0 0
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## 183 39 1 0 1 1 0 1 0 0 0
## 184 34 0 1 1 1 1 1 0 0 0
## 185 55 1 1 1 1 1 1 1 0 0
## 186 63 1 0 0 1 0 0 0 0 1
## 187 39 0 0 1 0 0 1 1 0 1
## 188 24 0 1 1 0 1 1 0 0 0
## 189 24 0 1 1 1 1 1 0 0 1
## 190 34 0 1 1 1 1 1 0 0 0
## 191 31 0 1 1 1 0 1 0 0 1
## 192 31 0 1 1 1 0 1 1 0 1
## 193 38 1 1 1 0 0 1 1 0 0
## 194 63 1 1 1 1 1 0 1 0 1
## 195 18 1 1 1 0 0 1 0 0 1
## 196 39 0 0 1 0 0 1 1 0 1
## 197 16 1 1 0 0 1 0 0 0 0
## 198 9 1 0 0 1 1 0 0 0 0
## 199 27 1 1 1 1 1 1 0 0 0
## 200 28 0 1 1 0 0 0 1 0 0
## 201 3 0 1 1 1 1 1 0 0 1
## 202 63 1 1 1 0 1 1 0 0 0
## 203 35 1 1 1 0 1 1 0 1 0
## 204 21 0 1 1 0 0 0 0 0 0
## 205 59 0 1 0 0 1 1 0 0 0
## 206 16 0 0 1 0 1 1 0 0 0
## 207 41 0 1 1 0 0 1 0 0 1
## 208 26 0 1 0 0 1 1 0 0 0
## 209 25 1 1 0 0 1 1 1 0 0
## 210 22 1 1 1 0 1 1 0 0 0
## 211 17 0 1 0 0 0 1 0 0 1
## 212 39 0 1 1 0 0 1 0 0 0
## 213 19 1 1 1 0 0 1 0 0 1
## 214 24 0 1 1 0 0 1 0 0 0
## 215 43 1 0 0 1 1 1 1 0 1
## 216 41 0 1 1 0 0 0 1 0 1
## 217 25 1 1 0 0 1 1 1 0 0
## 218 35 0 1 0 0 1 1 0 0 0
## 219 18 1 1 1 0 0 1 0 0 1
## 220 16 0 0 1 0 1 1 0 0 0
## 221 27 1 1 1 1 1 1 0 0 0
## 222 18 0 0 0 0 0 1 0 0 0
## 223 59 0 1 0 0 1 1 0 0 0
## 224 8 0 1 1 0 1 0 0 1 0
## 225 19 1 1 1 0 0 1 0 0 1
## 226 21 0 1 1 1 1 1 0 0 0
## 227 35 1 1 1 0 1 1 0 1 0
## 228 63 1 1 1 1 1 0 1 0 1
## 229 20 1 1 1 1 1 1 0 0 1
## 230 15 1 1 1 0 0 0 0 0 1
## 231 52 0 1 0 1 0 0 1 0 0
## 232 19 1 1 1 0 1 0 0 0 0
## 233 38 0 1 1 1 0 1 0 0 0
## 234 18 0 0 0 0 0 1 0 0 0
## 235 53 1 1 1 1 1 1 1 0 1
## 236 24 1 0 1 0 0 0 0 0 1
## 237 31 0 1 1 1 0 1 0 0 1
## 238 25 1 0 1 0 0 0 0 0 0
## Convulsion Anemia jundice cocacola_urine hypoglycemia prostraction
## 1 0 0 1 0 0 0
## 2 0 0 1 1 1 0
## 3 0 0 1 0 1 0
## 4 0 0 0 0 1 1
## 5 1 1 0 1 1 0
## 6 0 1 1 1 0 0
## 7 1 0 1 1 1 1
## 8 1 0 1 0 1 0
## 9 0 0 0 1 0 1
## 10 0 1 1 1 0 1
## 11 1 0 0 1 1 1
## 12 0 0 0 1 0 1
## 13 0 0 0 0 1 0
## 14 0 1 0 1 1 0
## 15 1 1 0 0 1 1
## 16 0 1 0 0 1 0
## 17 0 0 1 0 1 1
## 18 0 0 0 0 1 1
## 19 1 0 1 0 1 1
## 20 1 0 1 1 0 1
## 21 0 0 1 0 1 1
## 22 1 1 1 0 1 0
## 23 0 1 1 1 0 0
## 24 1 1 1 1 1 0
## 25 0 0 0 1 1 1
## 26 1 0 0 0 1 1
## 27 1 0 1 1 1 0
## 28 1 1 1 0 0 0
## 29 0 0 1 1 0 0
## 30 0 0 0 0 1 0
## 31 1 0 0 1 1 0
## 32 1 1 1 0 0 0
## 33 0 0 0 1 1 0
## 34 0 0 0 1 1 0
## 35 0 0 0 0 1 1
## 36 0 1 0 0 1 1
## 37 0 1 1 0 1 0
## 38 1 1 1 0 0 0
## 39 0 1 1 1 1 0
## 40 1 0 1 0 1 1
## 41 0 0 0 1 0 0
## 42 1 0 0 0 1 1
## 43 0 1 1 1 1 1
## 44 0 0 0 0 1 1
## 45 1 0 1 1 0 1
## 46 0 0 1 0 0 0
## 47 0 0 1 1 0 1
## 48 0 0 0 1 1 0
## 49 0 0 1 1 1 0
## 50 0 0 1 1 1 1
## 51 1 0 1 1 0 0
## 52 0 0 0 0 1 0
## 53 1 0 1 1 1 0
## 54 0 0 1 1 0 0
## 55 0 1 1 0 1 1
## 56 0 0 1 0 1 1
## 57 1 1 1 1 1 0
## 58 1 1 1 0 0 0
## 59 0 1 1 1 0 1
## 60 1 1 1 0 0 0
## 61 0 0 0 1 1 0
## 62 0 0 1 0 0 0
## 63 1 1 1 0 0 0
## 64 0 0 1 0 0 0
## 65 1 1 1 1 1 0
## 66 1 1 1 0 1 0
## 67 0 0 0 1 1 0
## 68 0 1 0 1 1 0
## 69 0 1 1 0 0 0
## 70 0 0 1 0 1 0
## 71 1 0 1 0 1 0
## 72 1 0 1 0 1 1
## 73 1 1 1 1 1 0
## 74 0 1 0 0 1 1
## 75 0 0 1 1 1 0
## 76 0 1 0 1 1 0
## 77 0 0 0 1 1 0
## 78 0 1 1 1 1 1
## 79 0 0 1 1 0 1
## 80 0 1 1 1 1 0
## 81 0 0 0 1 0 0
## 82 0 1 1 0 1 0
## 83 0 1 1 0 1 0
## 84 0 0 0 0 1 0
## 85 0 0 1 0 1 0
## 86 1 0 1 0 1 0
## 87 0 0 0 1 1 0
## 88 1 0 1 1 1 0
## 89 0 0 1 0 0 0
## 90 1 1 1 0 1 0
## 91 0 1 1 1 1 0
## 92 0 1 1 1 1 0
## 93 0 1 1 0 1 1
## 94 1 0 1 0 1 0
## 95 0 1 1 0 1 0
## 96 0 0 1 0 1 1
## 97 1 1 1 0 1 0
## 98 0 1 1 0 1 1
## 99 0 0 1 0 1 1
## 100 0 0 1 0 1 1
## 101 1 1 1 0 1 0
## 102 0 1 1 0 1 1
## 103 0 1 0 1 1 0
## 104 0 0 1 1 1 0
## 105 0 0 1 1 1 0
## 106 0 0 0 1 0 1
## 107 0 0 1 0 1 0
## 108 0 0 1 1 1 0
## 109 1 1 1 1 1 0
## 110 0 0 0 1 0 0
## 111 0 0 1 1 1 0
## 112 1 0 0 1 1 0
## 113 0 0 1 1 0 0
## 114 1 0 0 1 1 0
## 115 0 1 1 0 1 0
## 116 0 0 1 1 1 0
## 117 0 0 1 0 1 0
## 118 0 0 0 1 0 1
## 119 0 0 1 1 1 0
## 120 1 0 0 1 1 0
## 121 0 1 1 1 1 1
## 122 0 0 1 0 1 1
## 123 0 0 1 1 1 1
## 124 1 1 0 1 1 1
## 125 1 0 0 1 1 0
## 126 0 0 0 0 1 0
## 127 1 0 1 1 1 0
## 128 0 0 1 1 1 0
## 129 0 1 0 1 1 0
## 130 0 1 0 1 1 0
## 131 0 0 1 1 1 0
## 132 0 1 1 1 0 0
## 133 1 1 0 1 1 1
## 134 0 0 1 0 1 0
## 135 0 1 0 1 1 0
## 136 0 0 1 1 1 0
## 137 0 0 1 1 1 0
## 138 1 0 1 1 1 1
## 139 0 0 1 0 1 1
## 140 0 0 0 0 1 0
## 141 0 0 1 1 1 0
## 142 1 0 1 0 1 0
## 143 0 0 1 1 1 1
## 144 1 0 0 1 1 0
## 145 0 1 1 1 1 0
## 146 0 0 1 1 0 0
## 147 0 0 1 1 1 0
## 148 0 0 0 1 1 0
## 149 0 0 0 1 0 0
## 150 0 0 0 0 1 0
## 151 0 0 0 1 1 0
## 152 0 0 1 1 1 1
## 153 0 0 1 1 1 0
## 154 0 1 0 1 0 0
## 155 1 0 0 1 1 0
## 156 0 1 1 1 1 0
## 157 0 1 1 0 1 0
## 158 0 1 0 0 1 0
## 159 0 1 1 1 1 1
## 160 0 0 1 0 1 0
## 161 1 0 0 1 1 0
## 162 1 0 1 0 1 0
## 163 0 0 0 1 1 0
## 164 0 0 1 1 1 0
## 165 1 0 1 1 1 0
## 166 0 0 1 1 1 0
## 167 1 0 0 1 1 0
## 168 1 1 1 0 1 1
## 169 1 0 1 0 1 0
## 170 1 0 1 0 1 0
## 171 0 0 1 0 1 0
## 172 0 0 0 1 1 0
## 173 0 0 0 1 1 0
## 174 0 1 1 1 1 1
## 175 1 0 0 1 1 0
## 176 0 1 0 0 1 0
## 177 1 0 0 1 1 0
## 178 0 0 1 1 1 0
## 179 0 0 1 1 1 1
## 180 0 0 0 1 1 0
## 181 0 1 1 0 1 0
## 182 0 0 1 0 1 0
## 183 1 0 1 0 1 0
## 184 0 0 1 1 1 0
## 185 1 0 1 0 1 0
## 186 1 0 1 1 1 0
## 187 0 1 1 1 1 0
## 188 0 0 1 0 1 0
## 189 0 0 1 0 1 0
## 190 0 0 1 1 1 0
## 191 0 0 1 0 1 1
## 192 0 1 1 0 1 0
## 193 1 0 1 1 1 0
## 194 1 0 1 1 1 0
## 195 0 0 1 1 1 0
## 196 0 1 1 1 1 0
## 197 0 0 0 1 0 0
## 198 0 0 0 0 1 0
## 199 0 0 0 1 0 1
## 200 0 1 1 1 1 0
## 201 1 0 0 1 1 0
## 202 0 1 0 0 1 0
## 203 0 1 1 1 1 1
## 204 1 0 0 1 1 0
## 205 0 1 0 1 1 0
## 206 0 1 1 1 1 0
## 207 0 0 0 1 1 0
## 208 0 0 0 1 1 0
## 209 1 0 1 0 1 0
## 210 0 1 1 0 1 1
## 211 0 0 0 1 1 0
## 212 0 0 1 0 1 1
## 213 1 1 1 1 1 0
## 214 0 0 1 1 1 0
## 215 0 0 0 0 1 0
## 216 0 0 0 1 0 0
## 217 1 0 1 0 1 0
## 218 0 0 1 0 1 0
## 219 0 0 1 1 1 0
## 220 0 1 1 1 1 0
## 221 0 0 0 1 0 1
## 222 0 0 1 1 1 0
## 223 0 1 0 1 1 0
## 224 0 1 1 1 1 0
## 225 1 1 1 1 1 0
## 226 1 0 0 1 1 0
## 227 0 1 1 1 1 1
## 228 1 0 1 1 1 0
## 229 0 0 1 0 1 0
## 230 0 0 1 0 1 0
## 231 0 1 0 0 1 0
## 232 0 1 0 0 1 0
## 233 0 0 0 0 1 0
## 234 0 0 1 1 1 0
## 235 1 0 0 1 1 0
## 236 0 0 1 1 1 1
## 237 0 0 1 0 1 1
## 238 1 0 1 1 1 0
## hyperpyrexia severe_maleria
## 1 0 Not Severe
## 2 1 Not Severe
## 3 0 Not Severe
## 4 0 Not Severe
## 5 0 Not Severe
## 6 0 Not Severe
## 7 0 Not Severe
## 8 0 Not Severe
## 9 0 Not Severe
## 10 1 Not Severe
## 11 1 Not Severe
## 12 1 Not Severe
## 13 0 Not Severe
## 14 0 Not Severe
## 15 0 Not Severe
## 16 0 Not Severe
## 17 0 Not Severe
## 18 0 Not Severe
## 19 0 Not Severe
## 20 1 Not Severe
## 21 0 Not Severe
## 22 0 Not Severe
## 23 0 Not Severe
## 24 0 Not Severe
## 25 0 Not Severe
## 26 0 Not Severe
## 27 0 Not Severe
## 28 0 Not Severe
## 29 1 Not Severe
## 30 0 Not Severe
## 31 0 Not Severe
## 32 1 Not Severe
## 33 1 Not Severe
## 34 0 Not Severe
## 35 0 Not Severe
## 36 1 Not Severe
## 37 0 Not Severe
## 38 0 Not Severe
## 39 1 Not Severe
## 40 0 Not Severe
## 41 0 Not Severe
## 42 0 Not Severe
## 43 0 Not Severe
## 44 0 Not Severe
## 45 1 Not Severe
## 46 0 Not Severe
## 47 0 Not Severe
## 48 0 Not Severe
## 49 0 Not Severe
## 50 0 Not Severe
## 51 0 Not Severe
## 52 0 Not Severe
## 53 0 Not Severe
## 54 1 Not Severe
## 55 1 Not Severe
## 56 0 Not Severe
## 57 0 Not Severe
## 58 0 Not Severe
## 59 0 Not Severe
## 60 1 Not Severe
## 61 0 Not Severe
## 62 0 Not Severe
## 63 1 Not Severe
## 64 0 Not Severe
## 65 1 Not Severe
## 66 0 Not Severe
## 67 1 Not Severe
## 68 0 Not Severe
## 69 0 Not Severe
## 70 1 Not Severe
## 71 0 Not Severe
## 72 0 Not Severe
## 73 1 Not Severe
## 74 1 Not Severe
## 75 0 Not Severe
## 76 0 Not Severe
## 77 0 Not Severe
## 78 0 Not Severe
## 79 0 Not Severe
## 80 1 Not Severe
## 81 0 Not Severe
## 82 0 Not Severe
## 83 0 Not Severe
## 84 1 Not Severe
## 85 0 Not Severe
## 86 0 Not Severe
## 87 0 Not Severe
## 88 0 Not Severe
## 89 0 Not Severe
## 90 0 Not Severe
## 91 0 Not Severe
## 92 0 Not Severe
## 93 1 Not Severe
## 94 0 Not Severe
## 95 0 Not Severe
## 96 0 Not Severe
## 97 0 Not Severe
## 98 0 Not Severe
## 99 0 Not Severe
## 100 0 Not Severe
## 101 0 Not Severe
## 102 0 Not Severe
## 103 0 Not Severe
## 104 0 Not Severe
## 105 0 Not Severe
## 106 0 Not Severe
## 107 0 Not Severe
## 108 1 Not Severe
## 109 0 Not Severe
## 110 0 Severe
## 111 0 Severe
## 112 0 Severe
## 113 0 Severe
## 114 0 Severe
## 115 0 Severe
## 116 0 Severe
## 117 0 Severe
## 118 0 Severe
## 119 0 Severe
## 120 0 Severe
## 121 0 Severe
## 122 0 Severe
## 123 1 Severe
## 124 1 Severe
## 125 0 Severe
## 126 0 Severe
## 127 0 Severe
## 128 0 Severe
## 129 0 Severe
## 130 0 Severe
## 131 0 Severe
## 132 1 Severe
## 133 1 Severe
## 134 0 Severe
## 135 0 Severe
## 136 0 Severe
## 137 0 Severe
## 138 0 Severe
## 139 0 Severe
## 140 0 Severe
## 141 0 Severe
## 142 0 Severe
## 143 0 Severe
## 144 0 Severe
## 145 0 Severe
## 146 0 Severe
## 147 0 Severe
## 148 1 Severe
## 149 0 Severe
## 150 0 Severe
## 151 0 Severe
## 152 0 Severe
## 153 0 Severe
## 154 0 Severe
## 155 0 Severe
## 156 0 Severe
## 157 0 Severe
## 158 1 Severe
## 159 0 Severe
## 160 0 Severe
## 161 0 Severe
## 162 1 Severe
## 163 1 Severe
## 164 0 Severe
## 165 0 Severe
## 166 0 Severe
## 167 0 Severe
## 168 0 Severe
## 169 1 Severe
## 170 0 Severe
## 171 0 Severe
## 172 0 Severe
## 173 0 Severe
## 174 0 Severe
## 175 0 Severe
## 176 0 Severe
## 177 0 Severe
## 178 0 Severe
## 179 1 Severe
## 180 0 Severe
## 181 0 Severe
## 182 0 Severe
## 183 0 Severe
## 184 0 Severe
## 185 1 Severe
## 186 0 Severe
## 187 0 Severe
## 188 0 Severe
## 189 0 Severe
## 190 0 Severe
## 191 0 Severe
## 192 0 Severe
## 193 0 Severe
## 194 0 Severe
## 195 0 Severe
## 196 0 Severe
## 197 0 Severe
## 198 0 Severe
## 199 0 Severe
## 200 0 Severe
## 201 0 Severe
## 202 0 Severe
## 203 0 Severe
## 204 0 Severe
## 205 0 Severe
## 206 1 Severe
## 207 0 Severe
## 208 0 Severe
## 209 0 Severe
## 210 0 Severe
## 211 1 Severe
## 212 0 Severe
## 213 0 Severe
## 214 0 Severe
## 215 0 Severe
## 216 1 Severe
## 217 0 Severe
## 218 0 Severe
## 219 0 Severe
## 220 1 Severe
## 221 0 Severe
## 222 0 Severe
## 223 0 Severe
## 224 0 Severe
## 225 0 Severe
## 226 0 Severe
## 227 0 Severe
## 228 0 Severe
## 229 0 Severe
## 230 0 Severe
## 231 0 Severe
## 232 1 Severe
## 233 0 Severe
## 234 0 Severe
## 235 0 Severe
## 236 1 Severe
## 237 0 Severe
## 238 0 Severe
## Learning Vector Quantization
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## Pre-processing: centered (17), scaled (17)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## size k Accuracy Kappa
## 8 1 0.6847826 0.3423692
## 8 6 0.6891304 0.3656238
## 8 11 0.6126812 0.2006221
## 12 1 0.6304348 0.2333733
## 12 6 0.6597826 0.2909082
## 12 11 0.6429348 0.2566154
## 16 1 0.6684783 0.3165545
## 16 6 0.6648551 0.3049394
## 16 11 0.6846014 0.3493400
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 8 and k = 6.
## Reference
## Prediction Not Severe Severe
## Not Severe 14 12
## Severe 51 22
## Bagged CART
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results:
##
## Accuracy Kappa
## 0.8278986 0.6505981
## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Severe Severe
## Not Severe 35 16
## Severe 30 18
##
## Accuracy : 0.5354
## 95% CI : (0.4323, 0.6362)
## No Information Rate : 0.6566
## P-Value [Acc > NIR] : 0.99528
##
## Kappa : 0.0618
##
## Mcnemar's Test P-Value : 0.05527
##
## Sensitivity : 0.5294
## Specificity : 0.5385
## Pos Pred Value : 0.3750
## Neg Pred Value : 0.6863
## Precision : 0.3750
## Recall : 0.5294
## F1 : 0.4390
## Prevalence : 0.3434
## Detection Rate : 0.1818
## Detection Prevalence : 0.4848
## Balanced Accuracy : 0.5339
##
## 'Positive' Class : Severe
##
## Reference
## Prediction Not Severe Severe
## Not Severe 35 16
## Severe 30 18
## Boosted Classification Trees
##
## 238 samples
## 17 predictor
## 2 classes: 'Not Severe', 'Severe'
##
## Pre-processing: centered (17), scaled (17)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 214, 214, 214, 214, 214, 214, ...
## Resampling results across tuning parameters:
##
## maxdepth iter Accuracy Kappa
## 1 50 0.6177536 0.1918530
## 1 100 0.6224638 0.2165071
## 1 150 0.6309783 0.2380119
## 2 50 0.6896739 0.3574489
## 2 100 0.6769928 0.3361978
## 2 150 0.6980072 0.3808271
## 3 50 0.6856884 0.3453528
## 3 100 0.7318841 0.4463229
## 3 150 0.7442029 0.4757256
##
## Tuning parameter 'nu' was held constant at a value of 0.1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were iter = 150, maxdepth = 3 and nu = 0.1.
## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Severe Severe
## Not Severe 33 18
## Severe 32 16
##
## Accuracy : 0.4949
## 95% CI : (0.3929, 0.5973)
## No Information Rate : 0.6566
## P-Value [Acc > NIR] : 0.99967
##
## Kappa : -0.0198
##
## Mcnemar's Test P-Value : 0.06599
##
## Sensitivity : 0.4706
## Specificity : 0.5077
## Pos Pred Value : 0.3333
## Neg Pred Value : 0.6471
## Precision : 0.3333
## Recall : 0.4706
## F1 : 0.3902
## Prevalence : 0.3434
## Detection Rate : 0.1616
## Detection Prevalence : 0.4848
## Balanced Accuracy : 0.4891
##
## 'Positive' Class : Severe
##
## Reference
## Prediction Not Severe Severe
## Not Severe 33 18
## Severe 32 16
##
## Call:
## summary.resamples(object = results)
##
## Models: SVM, Bagging, LR, NB, RF, KNN, NN, LDA, LVQ, Boosting
## Number of resamples: 10
##
## Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## SVM 0.6250000 0.6354167 0.7028986 0.6936594 0.7472826 0.7500000 0
## Bagging 0.7083333 0.7916667 0.8297101 0.8278986 0.8736413 0.9166667 0
## LR 0.5833333 0.6250000 0.6250000 0.6476449 0.6630435 0.7826087 0
## NB 0.5416667 0.5833333 0.6250000 0.6266304 0.6884058 0.7083333 0
## RF 0.7083333 0.8278986 0.8541667 0.8442029 0.8750000 0.9166667 0
## KNN 0.4166667 0.5833333 0.6385870 0.6224638 0.6979167 0.7391304 0
## NN 0.6250000 0.6739130 0.7445652 0.7601449 0.8541667 0.9166667 0
## LDA 0.5833333 0.6250000 0.6250000 0.6476449 0.6630435 0.7826087 0
## LVQ 0.5652174 0.6250000 0.6458333 0.6891304 0.7812500 0.8333333 0
## Boosting 0.5833333 0.7160326 0.7500000 0.7442029 0.7916667 0.8695652 0
##
## Kappa
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## SVM 0.20588235 0.2623175 0.3830227 0.3676600 0.4808290 0.4893617 0
## Bagging 0.40000000 0.5697464 0.6592355 0.6505981 0.7469106 0.8321678 0
## LR 0.14893617 0.2285714 0.2500000 0.2838780 0.3124128 0.5525292 0
## NB 0.05714286 0.1548056 0.2285714 0.2398071 0.3574938 0.4166667 0
## RF 0.40000000 0.6566854 0.7035964 0.6853067 0.7464789 0.8321678 0
## KNN -0.20863309 0.1272384 0.2464909 0.2144296 0.3682958 0.4651163 0
## NN 0.20588235 0.3363199 0.4868881 0.5082464 0.7027163 0.8321678 0
## LDA 0.14893617 0.2285714 0.2500000 0.2838780 0.3124128 0.5525292 0
## LVQ 0.12408759 0.2312877 0.2743946 0.3656238 0.5444166 0.6595745 0
## Boosting 0.13669065 0.4203008 0.4820144 0.4757256 0.5759557 0.7396226 0
