Table 8.

Classification performance evaluation of feature selection with different top k features selected.

Model	k	Accuracy	Precision	F1 Score	TPR	TNR	auROC	auPR	MCC
SVM	20	89.56	88.79	0.8895	0.72	0.92	0.88	0.694	0.5675
	15	89.78	89.03	0.8918	0.72	0.92	0.877	0.701	0.5699
	10	89.51	88.76	0.8895	0.71	0.92	0.85	0.663	0.5619
RF	20	89.1	88.11	0.8787	0.75	0.91	0.932	0.743	0.5898
	15	89.13	88.14	0.8814	0.73	0.91	0.928	0.736	0.6025
	10	89.51	89.09	0.8926	0.68	0.93	0.923	0.707	0.5802
MLP	20	88.97	88.37	0.8859	0.67	0.92	0.902	0.669	0.5516
	15	88.4	87.44	0.877	0.67	0.91	0.898	0.622	0.5113
	10	88.23	87.73	0.8786	0.62	0.92	0.861	0.587	0.5291
DT	20	90.54	89.9	0.8998	0.76	0.93	0.923	0.731	0.6049
	15	90.54	89.9	0.8998	0.76	0.93	0.923	0.731	0.6049
	10	89.89	89.64	0.8975	0.68	0.94	0.926	0.7014	0.6037
XGBoost	20	90.65	90.01	0.898	0.801	0.96	0.937	0.749	0.5992
	15	90.19	89.45	0.8928	0.7804	0.92	0.936	0.742	0.5775
	10	89.83	89.01	0.8889	0.76	0.91	0.935	0.731	0.5611