Table 3.

Comparison of the proposed methods' performance study.

Method	Classifiers	Accuracy (%)	Precision (%)	Recall (%)	F1-Score (%)	AUC (%)	Miss Rate (%)
PCA	LR	75.75	68.44	80.16	73.84	82.82	24.20
	RF	92.31	89.50	94.84	92.09	97.79	7.69
	KNN	88.75	80.65	96.23	87.76	93.95	11.20
	SVM	87.31	81.06	92.63	86.45	91.06	12.60
	GB	86.35	82.03	89.78	85.73	90.45	13.60
	XGB	91.52	87.58	95.08	91.18	96.07	8.48
FA	LR	78.80	73.52	82.20	77.62	86.96	21.20
	RF	91.70	89.84	93.43	91.60	97.71	8.30
	KNN	89.43	82.37	95.92	88.63	94.20	10.50
	SVM	83.88	77.64	88.71	82.80	91.80	16.10
	GB	84.80	79.83	88.65	84.01	91.61	15.20
	XGB	90.50	86.69	93.83	90.12	95.84	9.50
PCA-FA	LR	80.48	75.51	83.85	79.46	88.46	19.50
	RF	92.55	90.53	94.35	92.40	98.15	7.45
	KNN	89.54	82.16	96.37	88.70	94.16	10.40
	SVM	86.38	80.72	91.02	85.56	93.35	13.60
	GB	86.38	82.03	89.85	85.76	92.75	13.60
	XGB	91.52	87.58	95.08	91.18	97.37	8.48
FPCA	SVM	83.26	77.09	87.94	82.16	91.06	16.70
	RF	91.90	89.50	94.02	91.70	97.78	8.10
	XGB	90.02	87.01	93.44	89.61	96.07	9.98
	Stacking	92.28	91.29	93.48	92.37	97.76	7.72
Ensemble	SVM	90.36	85.18	95.02	89.83	95.21	9.64
Feature	RF	92.42	92.18	92.62	92.40	96.50	7.58
	XGB	94.78	94.37	95.15	94.76	98.78	5.58
	Stacking	94.89	94.43	95.40	94.91	98.88	5.11