Table 1.

Comparison of different feature sets using three classification learning models.

Features	Model	Accuracy	Sensitivity	Specificity	Precision	F-measure
	ANN	96.95	98.73	95.29	95.42	0.970
SDAE	SVM	98.04	97.21	99.11	99.17	0.981
	SVM-RBF	98.26	97.61	99.11	99.17	0.983
	ANN	63.04	60.56	70.76	84.58	0.704
DIFFEXP500	SVM	57.83	64.06	46.43	70.42	0.618
	SVM-RBF	77.391	86.69	71.29	67.08	0.755
	ANN	59.93	59.93	69.95	84.58	0.701
DIFFEXP0.05	SVM	68.70	82.73	57.5	65.04	0.637
	SVM-RBF	76.96	87.56	70.48	65.42	0.747
	ANN	96.52	98.38	95.10	95.00	0.965
PCA	SVM	96.30	94.58	98.61	98.75	0.965
	SVM-RBF	89.13	83.31	99.47	99.58	0.906
	ANN	97.39	96.02	99.10	99.17	0.975
KPCA	SVM	97.17	96.38	98.20	98.33	0.973
	SVM-RBF	97.32	89.92	99.52	99.58	0.943