Table 4.

10-fold CV Classification performance of different classifiers on selected features.

Classifier		Mean Accuracy	Mean Specificity (%)	Mean Sensitivity	Mean F- measure	Kappa Statistic (KS)	AUC
HGB Classifier	Mean	0.8176	0.814	0.8296	0.8201	82.4%	0.8233
	95% CI	(0.81, 0.83)	(0.8, 0.82)	(0.81, 0.85)	(0.81, 0.83)	(0.82, 0.86)	(0.81, 0.83)
	STD	0.0154	0.0127	0.0296	0.0148	0.0257	0.0157
Bagging Classifier	Mean	0.847	0.841	0.847	0.845	84.36%	0.843
	95% CI	(0.84, 0.85)	(0.84, 0.85)	(0.84, 0.85)	(0.85, 0.85)	(0.84, 0.85)	(0.84, 0.85)
	STD	0.0172	0.0116	0.00128	0.0194	0.0127	0.0182
MLP Classifier	Mean	0.886	0.889	0.884	0.881	88.6%	0.882
	95% CI	(0.88, 0.89)	(0.88, 0.89)	(0.88, 0.89)	(0.88, 0.89)	(0.88, 0.89)	(0.88, 0.89)
	STD	0.0027	0.0112	0.0134	0.00140	0.010	0.0129
XGBoost Classifier	Mean	0.917	0.913	0.916	0.918	91.37%	0.9145
	95% CI	(0.91, 0.92)	(0.91, 0.92)	(0.91, 0.92)	(0.91, 0.92)	(0.91, 0.92)	(0.91, 0.92)
	STD	0.0146	0.0138	0.0147	0.0175	0.01924	0.0126
SVM (kernel = linear)	Mean	0.8896	0.8733	0.912	0.892	88.7%	0.892
	95% CI	(0.87, 0.90)	(0.66, 0.88)	(0.90, 0.93)	(0.88, 0.90)	(0.88, 0.89)	(0.88, 0.90)
	STD	0.0174	0.0167	0.0129	0.0182	0.0140	0.01864
SVM (kernel = RBF)	Mean	0.857	0.850	0.861	0.859	86.7%	0.863
	95% CI	(0.85, 0.86)	(0.84, 0.86)	(0.85, 0.87)	(0.85, 0.87)	(0.86, 0.87)	(0.86, 0.87)
	STD	0.0127	0.01734	0.0129	0.0134	0.0118	0.01727
K Nearest Neighbor Classifier	Mean	0.8835	0.8785	0.892	0.8937	88.3%	0.886
	95% CI	(0.88, 0.89)	(0.87, 0.89)	(0.89, 0.90)	(0.89, 0.90)	(0.88, 0.89)	(0.88, 0.89)
	STD	0.0014	0.0174	0.018	0.0162	0.0183	0.0163