Table 2:

Performance measures for each ML model applied to the external testing dataset.

Model	AUC	TPR	FPR	PPV	NPV	F1 Score	Balanced Accuracy	Misclassification Error
BG	0.79	0.82	0.38	0.55	0.86	0.66	0.72	0.31
RF	0.82	0.91	0.36	0.58	0.92	0.71	0.77	0.27
SVM	0.78	0.87	0.52	0.48	0.87	0.62	0.68	0.38
KNN	0.76	0.89	0.53	0.49	0.91	0.63	0.69	0.37
LR	0.79	0.90	0.47	0.52	0.91	0.66	0.72	0.34

AUC=area under the ROC curve. TPR=true positive rate (sensitivity or recall = number of true positives divided by all positives). FPR=false positive rate (1-specificity = number of false positives divided by all negatives). PPV=positive predictive value (precision = number of true positives divided by number of true and false positives). NVP=negative predictive value (=number of true negatives divided by the number of true and false negatives). F1=2*PPV*TPR/(PPV+TPR)=harmonic mean of precision and recall. Balanced accuracy=accuracy accounting for class imbalance (=(sensitivity + specificity)/2). Misclassification error=number of incorrect classifications divided by sample size.