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Table 2: Performance measures for each machine learning model applied to the external testing data set^{^a}
Model  AUC TPR FPR PPV NPV F1 Score Balanced Accuracy Misclassification Error
BG 0.74 0.71 0.35 0.24 0.93 0.36 0.68 0.34
RF 0.76 0.71 0.32 0.26 0.94 0.38 0.70 0.32
SVM 0.84 0.93 0.35 0.30 0.98 0.45 0.79 0.31
KNN  0.76 0.79 0.36 0.26 0.95 0.39 0.71 0.34
LR 0.77 0.86 0.37 0.27 0.96 0.41 0.74 0.34
Note:—NPV indicates negative predictive value, the number of true-negatives divided by the number of true- and false-negatives; AUC, area under curve; 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); TPR, true-positive rate (sensitivity or recall = number of true-positives divided by all positives).
↵a F1 = 2 × PPV × TPR / (PPV + TPR) is the harmonic mean of precision and recall. Balanced accuracy is accuracy accounting for class imbalance [(sensitivity + specificity)/ 2]. Misclassification error is the number of incorrect classifications divided by sample size.