Table 5.

The performed accuracy (ACC), precision (PRE), recall (REC), f1-score (F1S), the area under the curve (AUC), and Cohen's Kappa score (CKS) for the Kaggle dataset.

Feature sets	Classifiers	ACC	PRE	REC	F1S	AUC	CKS
All features	RF	0.9561	0.9649	0.9390	0.9517	0.9866	0.9115
	DT	0.9465	0.9323	0.9477	0.9399	0.9704	0.8917
	KNN	0.9375	0.9047	0.9537	0.9286	0.9834	0.8713
	RDKVT	0.9618	0.9348	0.9790	0.9564	0.9952	0.9223
	RDKST	0.9774	0.9624	0.9896	0.9758	0.9957	0.9566
UVS features	RF	0.9662	0.9292	0.9946	0.9611	0.9940	0.9313
	DT	0.9622	0.9523	0.9632	0.9577	0.9867	0.9237
	KNN	0.8947	0.8797	0.8776	0.8836	0.8986	0.7875
	RDKVT	0.9741	0.9436	0.9986	0.9703	0.9958	0.9474
	RDKST	0.9774	0.9719	0.9749	0.9765	0.9979	0.9545
IGS features	RF	0.9746	0.9761	0.9677	0.9719	0.9939	0.9761
	DT	0.9718	0.9423	0.9947	0.9678	0.9889	0.9428
	KNN	0.9634	0.9260	0.9919	0.9578	0.9936	0.9256
	RDKVT	0.9831	0.9674	0.9948	0.9809	0.9990	0.9657
	RDKST	0.9898	0.9799	0.9974	0.9886	0.9995	0.9794