Table 6.

The results of ablation study: performance of the proposed model using different final stage ML classifiers. Best values are shown in bold.

ML Model	Accuracy (%)	FPR (%)	FNR (%)	AUC (%)	MCC (%)	Kappa (%)
Nearest Neighbors	78.9	39.86	11.02	74.56	51.48	50.8
Linear SVM	64.66	100	0	50	0	0
RBF SVM	71.44	79.06	1.72	59.62	26.34	21.66
Decision Tree	94.64	9.36	3.2	93.72	88.24	87.94
Random Forest	90.74	22.38	2.2	87.72	79.54	78.64
Neural Net	65.02	99.04	0	50.48	3.48	1.18
AdaBoost	99.28	2.24	0	98.88	98.36	98.32
ExtraTrees	99.28	0	1.04	99.48	98.4	98.4
Naive Bayes	72.14	54.14	13.78	66.06	35.48	34.26
LDA	70.34	67.62	8.86	61.8	30.08	26.44
QDA	91.44	18.4	3.3	89.14	81.26	80.46
Logistic	65.02	99.04	0	50.48	3.48	1.18
Passive	59.64	60	29.48	55.26	11.48	9.74
Ridge	67.18	92.2	0.52	53.62	17.24	8.82
SGDC	58.96	52.38	34.88	56.36	13.12	15.1