Table 3.

Performance of classification models by cross-validation.

	Accuracy (%)	Specificity (%)	Sensitivity (%)	Precision (%)	AUC
Support vector machine (SVM)	94.44 ± 5.86	100.00	92.14 ± 8.33	100.00	0.983 ± 0.053
Decision tree	95.56 ± 5.74	91.67 ± 18.00	96.67 ± 7.03	97.32 ± 5.66	0.550 ± 0.158
Naïve Bayes	94.44 ± 7.86	96.67 ± 10.54	93.57 ± 8.33	98.33 ± 5.27	0.967 ± 0.070
Logistic regression	91.22 ± 8.74	83.33 ± 32.39	93.57 ± 8.33	94.86 ± 8.59	0.961 ± 0.123
Ensemble vote	95.56 ± 5.74	100.00	93.57 ± 8.33	100.00	0.792 ± 0.252
Ensemble AdaBoost-SVM	94.44 ± 10.8	93.33 ± 21.08	95.24 ± 7.69	97.14 ± 9.04	0.986 ± 0.044
Ensemble bagging-SVM	94.44 ± 5.86	100.00	92.14 ± 8.33	100.00	0.983 ± 0.053