Table 1.

Model performances of the applied data-mining approaches for the training and validation datasets.

Parameters	LMT		NBT		LR		ANN		SVM
	T *	V *	T	V	T	V	T	V	T	V
True positive	78	19	77	18	80	19	76	16	77	18
True negative	81	19	83	20	81	19	73	17	83	20
False positive	11	3	12	4	9	3	13	6	12	4
False negative	8	3	6	2	8	3	16	5	6	2
Sensitivity (%)	0.907	0.864	0.928	0.900	0.909	0.864	0.826	0.762	0.928	0.900
Specificity (%)	0.880	0.864	0.874	0.833	0.900	0.864	0.849	0.739	0.874	0.833
Accuracy (%)	0.893	0.864	0.899	0.864	0.904	0.864	0.837	0.750	0.899	0.864
MAE	0.207	0.216	0.225	0.225	0.213	0.216	0.241	0.235	0.223	0.246
RMSE	0.304	0.313	0.319	0.341	0.311	0.314	0.349	0.358	0.318	0.369
AUC	0.944	0.936	0.918	0.874	0.939	0.936	0.911	0.871	0.899	0.864

T *: Training, V *: Validation.