Table 4.

Classification results with full datasets

Dataset	Classifier	Accuracy (%)	Precision (%)	Sensitivity (%)	Specificity (%)	F1-score (%)	AUC (%)
Dataset 1	MLP	79.96	82.40	79.67	80.29	81.01	79.98
	SVM	75.72	78.70	75.10	76.44	76.86	75.77
	LR	77.51	80.70	76.35	78.85	78.46	77.60
	Decision tree	79.69	79.38	84.65	74.52	81.93	79.58
	Gradient boosting	80.40	81.22	82.57	77.88	81.89	80.23
	Random forest	81.51	83.19	82.16	80.77	82.67	81.46
	XGboost	80.40	81.22	82.57	77.88	81.89	80.23
	AdaBoost	79.96	81.86	80.50	79.33	81.17	79.91
Dataset 2	MLP	89.36	89.36	89.36	90.01	89.36	89.68
	SVM	92.62	92.62	92.62	93.90	92.62	93.26
	LR	92.88	92.88	92.88	94.28	92.88	93.58
	Decision tree	85.96	85.96	85.96	86.27	85.96	86.12
	Gradient boosting	92.41	92.41	92.41	93.95	92.41	93.18
	Random forest	89.36	89.36	89.36	90.01	89.36	89.68
	XGboost	92.36	92.36	92.36	93.94	92.36	93.15
	AdaBoost	89.35	89.35	89.35	90.01	89.35	89.68

Bold values highlight the best results for the two studied datasets