Table 2.

Results of discriminative RMs in group 1 in the testing group.

	QBDT		Xgboost		RF		Distance correlation		LASSO
	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC
SVM	0.806	0.876	0.761	0.841	0.761	0.878	0.761	0.779	0.722	0.787
KNN	0.776	0.823	0.766	0.832	0.771	0.853	0.761	0.804	0.731	0.751
LDA	0.771	0.814	0.772	0.849	0.781	0.829	0.767	0.730	0.752	0.819
GausiannNB	0.761	0.847	0.746	0.824	0.721	0.831	0.756	0.799	0.601	0.740
Adaboost	0.731	0.828	0.761	0.819	0.716	0.769	0.701	0.740	0.711	0.786
LR	0.771	0.829	0.757	0.791	0.751	0.796	0.757	0.715	0.697	0.775
DT	0.711	0.706	0.756	0.746	0.721	0.714	0.672	0.658	0.682	0.668

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.