Table 7.

Cross-validation and testing performance metrics for the four selected machine learning models.

Model	Cross-validation metrics						Testing metrics
	AUC	Accuracy %	TPR, %	TNR, %	PPV, %	NPV, %	Accuracy, %	TPR, %	TNR, %	PPV, %	NPV, %
FINE KNN	0.84	84.1	85	83	84	84	85.7	88	84	84	87
WEIGHTED KNN	0.90	81.2	83	79	80	82	82.7	84	81	82	84
FINE GAUSSIAN SVM	0.90	82.2	80	84	83	81	83.0	81	85	85	82
BAGGED TREE	0.92	83.7	82	85	85	83	84.0	81	88	87	82

AUC, areas under the ROC curves; NPV, negative predictive values; PPV, positive predictive values; TPR, true-positive rates/sensitivities; TNR, true-negative rates/specificities.Numbers in bold indicate the values of the model that provided the highest combination of AUC and validation accuracy.