Table 3.

Overall comparisons for methods.

	ACC	GM	ERR	SENS	SPEC	F-M	MCC	YI	Kappa	FPR	ROC
Bayesian Network	0.887	0.882	0.113	0.887	0.976	0.887	0.862	0.863	0.828	0.024	0.970
Neural Network	0.828	0.826	0.172	0.828	0.963	0.828	0.787	0.791	0.741	0.037	0.953
Logistic Regression	0.819	0.817	0.181	0.819	0.960	0.819	0.772	0.778	0.727	0.040	0.951
Naive Bayes	0.799	0.800	0.201	0.799	0.940	0.799	0.736	0.739	0.693	0.060	0.951
J48	0.804	0.804	0.196	0.804	0.958	0.804	0.752	0.762	0.705	0.042	0.930
Support Vector Machine (SVM)	0.828	0.826	0.172	0.828	0.962	0.828	0.784	0.790	0.741	0.038	0.916
KStar	0.838	0.835	0.162	0.838	0.963	0.838	0.794	0.801	0.756	0.037	0.952
k-Nearest Neighbor (k-NN)	0.809	0.808	0.191	0.809	0.958	0.809	0.756	0.766	0.715	0.042	0.943