Table 5.

Decision matrix of the proposed method for symptomatic category considering training strategies. Evaluation criteria into two groups based on maximization and minimization. Acc., AUC, Precision, Recall, Specificity, F1-score are expected to be the maximum; in contrast, FPR and FNR are expected to have the minimum.

Training strategies	Classifiers	Evaluation Criteria
		Acc.(↑)	AUC(↑)	Precision(↑)	Recall(↑)	Specificity(↑)	F1-score(↑)	FPR(↓)	FNR(↓)
Strategy 1	Extra-Trees	0.87	0.87	1	0.8	1	0.89	0	0.20
	SVM	0.79	0.78	0.93	0.72	0.91	0.81	0.09	0.28
	RF	0.87	0.87	0.96	0.83	0.94	0.89	0.06	0.17
	AdBoost	0.79	0.78	0.93	0.72	0.91	0.81	0.09	0.28
	MLP	0.83	0.81	0.98	0.74	0.97	0.84	0.03	0.26
	XGBoost	0.84	0.81	0.95	0.78	0.94	0.86	0.06	0.22
	GBoost	0.79	0.75	0.93	0.72	0.91	0.81	0.09	0.28
	LR	0.84	0.8	0.95	0.78	0.94	0.86	0.06	0.22
	k-NN	0.73	0.75	0.92	0.63	0.91	0.75	0.09	0.37
	HGBoost	0.77	0.70	0.87	0.74	0.81	0.80	0.19	0.26
Strategy 2	Extra-Trees	0.86	0.86	0.98	0.80	0.97	0.88	0.03	0.20
	SVM	0.84	0.80	0.92	0.81	0.88	0.86	0.13	0.19
	RF	0.84	0.85	0.95	0.78	0.94	0.86	0.06	0.22
	AdBoost	0.80	0.79	0.91	0.76	0.88	0.83	0.13	0.24
	MLP	0.87	0.86	0.94	0.85	0.91	0.89	0.09	0.15
	XGBoost	0.81	0.84	1	0.70	1	0.83	0	0.30
	GBoost	0.86	0.83	0.94	0.83	0.91	0.88	0.09	0.17
	LR	0.86	0.83	0.89	0.89	0.81	0.89	0.19	0.11
	k-NN	0.72	0.74	0.97	0.57	0.97	0.72	0.03	0.43
	HGBoost	0.77	0.74	0.93	0.69	0.91	0.79	0.09	0.31
Strategy 3	Extra-Trees	0.84	0.83	1	0.74	1	0.85	0	0.26
	SVM	0.80	0.79	0.93	0.74	0.91	0.82	0.09	0.26
	RF	0.87	0.85	0.96	0.83	0.94	0.89	0.06	0.17
	AdBoost	0.83	0.80	0.95	0.76	0.94	0.85	0.06	0.24
	MLP	0.83	0.78	0.88	0.83	0.81	0.86	0.19	0.17
	XGBoost	0.84	0.81	0.92	0.81	0.88	0.86	0.13	0.19
	GBoost	0.88	0.87	0.98	0.83	0.97	0.90	0.03	0.17
	LR	0.78	0.76	0.95	0.69	0.94	0.80	0.06	0.31
	k-NN	0.69	0.71	0.97	0.52	0.97	0.67	0.03	0.48
	HGBoost	0.83	0.80	0.91	0.80	0.88	0.85	0.13	0.20