Table 3.

Performance results for machine learning algorithms using k-fold cross validation method.

Classifier	Accuracy (%)	Sensitivity (%)	Specificity (%)	Kappa	MCC	ROC_AUC
Decision tree	74.42	71.05	77.79	0.4884	0.4895	0.80
Discriminant analysis	73.11	52.42	93.79	0.4621	0.5076	0.80
Gentle boost	79.26	76.95	81.58	0.5853	0.5859	0.88
k-nearest-neighbors	74.00	66.32	81.68	0.480	0.4858	0.83
Logistic regression	75.11	69.16	81.05	0.5021	0.5057	0.86
Naive Bayes	69.84	51.37	88.32	0.3968	0.4271	0.78
Artificial neural network	82.32	80.53	84.11	0.6463	0.6467	0.91
Support vector machine	81.05	76.42	85.68	0.6211	0.6237	0.91