Table 4.

Test data sets for each model evaluation index.

Items	E-RFa	E-SVMb	F-RFc	F-SVMd
Misclassification rate	0.28	0.20	0.27	0.22
Sensitivity	0.48	0.68	0.48	0.69
Specificity	0.86	0.87	0.86	0.83
Positive predictive value	0.63	0.72	0.63	0.68
Negative predictive value	0.76	0.84	0.76	0.84
Geometric mean	0.84	0.76	0.64	0.76
ROC-AUCe	0.75	0.81	0.70	0.78

^aE-RF: model built using the random algorithm and expert consultation method.

^bE-SVM: model built using the support vector machine algorithm and expert consultation method.

^cF-RF: model built using the random forest algorithm and random forest-based filter feature selection method.

^dF-SVM: model built using the support vector machine algorithm and Random forest-based filter feature selection method.

^eROC-AUC: receiver operating characteristic curve-area under the curve.