Table 4.

Results of discriminative RMs in group 2 in the testing group.

	QBDT		Xgboost		RF		Distance correlation		LASSO
	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC
SVM	0.695	0.760	0.645	0.702	0.636	0.684	0.655	0.699	0.605	0.570
KNN	0.628	0.658	0.639	0.634	0.611	0.669	0.654	0.655	0.545	0.502
LDA	0.705	0.730	0.679	0.721	0.611	0.702	0.696	0.740	0.620	0.663
GausiannNB	0.638	0.747	0.689	0.717	0.628	0.733	0.604	0.721	0.546	0.576
Adaboost	0.687	0.666	0.630	0.638	0.595	0.598	0.637	0.599	0.605	0.582
LR	0.696	0.763	0.680	0.732	0.670	0.722	0.688	0.720	0.664	0.587
DT	0.613	0.602	0.637	0.630	0.608	0.570	0.561	0.532	0.546	0.524

AUC, Area under curve; Decision tree, DT; GBDT, Gradient boosting decision tree; KNN, K-nearest neighbor; LASSO, Least absolute shrinkage and selection operator; LDA, Linear Discriminant analysis; LR, Logistic regression; RF, Random forest; SVM, Support vector machine; Xgboost, Extreme gradient boosting.