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Table 3 Accuracy and AUC values of all models

From: Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

  hold out 80/20 hold out 70/30 hold out 60/40 CV 5 fold CV 10 fold CV 15 fold bootstrap 50 bootstrap 100 bootstrap 200
Numerical IHD Dataset
 Logistic regression Accuracy 0.7018 0.7529 0.7368 0.7386 0.7278 0.7207 0.6223 0.7486 0.7470
AUC 0.5374 0.6392 0.6105 0.6096 0.5951 0.5597 0.5388 0.6343 0.6102
 Naive Bayes Accuracy 0.7544 0.7412 0.7719 0.7245 0.7319 0.7312 0.6524 0.7705 0.7590
AUC 0.7392 0.7791 0.7999 0.6591 0.6740 0.7041 0.4966 0.6786 0.7719
 CART Accuracy 0.7719 0.7059 0.6930 0.7031 0.7036 0.7060 0.6567 0.7377 0.7349
AUC 0.6827 0.3797 0.6999 0.6787 0.6097 0.6587 0.4495 0.6862 0.6694
 C4.5 Accuracy 0.7193 0.7294 0.7544 0.7563 0.7284 0.7528 0.7167 0.7268 0.6747
AUC 0.6520 0.6792 0.7569 0.6727 0.6991 0.7480 0.4422 0.6151 0.6233
 C5.0 Accuracy 0.6667 0.6824 0.7544 0.7246 0.7318 0.7493 0.6652 0.7268 0.6747
AUC 0.6478 0.7132 0.7716 0.6514 0.7150 0.7146 0.4847 0.3861 0.6498
 C5.0 boosted Accuracy 0.7018 0.7882 0.7544 0.7706 0.7499 0.7596 0.6695 0.7268 0.7590
AUC 0.6420 0.7710 0.7686 0.7415 0.7130 0.7510 0.6600 0.6988 0.7849
 Random Forest Accuracy 0.7544 0.7765 0.8421 0.8023 0.8025 0.8123 0.7639 0.8033 0.7952
AUC 0.8181 0.8524 0.8630 0.7943 0.8268 0.8274 0.6923 0.8538 0.8533
Categrical IHD dataset
 Logistic regression Accuracy 0.6842 0.7411 0.7544 0.7563 0.7493 0.7483 0.5794 0.7377 0.7349
AUC 0.5257 0.6448 0.6220 0.6255 0.6442 0.6322 0.5535 0.6191 0.6179
 Naive Bayes Accuracy 0.7544 0.7412 0.7632 0.7245 0.7319 0.7312 0.6481 0.7650 0.7590
AUC 0.7359 0.7812 0. 7986 0.6565 0.6745 0.7012 0.4976 0.6580 0.7711
 CART Accuracy 0.7368 0.7059 0.6930 0.7031 0.7108 0.7097 0.6567 0.7377 0.7349
AUC 0.6653 0.3797 0. 6999 0.6787 0.5971 0.6575 0.4495 0.6862 0.6694
 C4.5 Accuracy 0.7193 0.7412 0.7632 0.7456 0.7461 0.7774 0.7554 0.6831 0.7108
AUC 0.4427 0.7037 0. 7580 0.6784 0.6818 0.7365 0.4641 0.5457 0.6575
 C5.0 Accuracy 0.7193 0.6706 0.7544 0.7316 0.7459 0.7528 0.6395 0.7268 0.6747
AUC 0.6171 0.6939 0. 7716 0.6775 0.6983 0.6994 0.6209 0.3861 0.6701
 C5.0 boosted Accuracy 0.7544 0.7529 0.7719 0.7598 0.7562 0.7943 0.6395 0.7541 0.7470
AUC 0.7575 0.7283 0. 7084 0.7318 0.7335 0.7947 0.6209 0.7169 0.7596
 Random Forest Accuracy 0.7719 0.7765 0.8509 0.8093 0.8130 0.8123 0.7811 0.8197 0.7952
AUC 0.8198 0.8597 0. 8636 0.7782 0.8194 0.8179 0.7064 0.8542 0.8322