Table 6.

Comparison of results achieved using various classifiers in combination with the proposed network. Results achieved using proposed classifier is shown in bold.

Classifier	Precision	Recall	F-measure	AUC	Accuracy
Bayesnet	0.853	0.985	0.914	0.963	91.1579
NaiveBayes	0.83	0.989	0.902	0.947	89.7895
SVM	0.822	0.989	0.898	0.897	89.2632
LogisticRegresion	0.846	0.909	0.877	0.941	87.7895
AdaBoostM1	0.81	0.998	0.894	0.898	88.7368
Random Forest	0.828	0.987	0.9	0.94	89.5789
ADTree	0.828	0.938	0.88	0.922	87.7895
NBTree	0.84	0.96	0.896	0.949	89.3684