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Table 1: 
Summary of evaluation metrics for machine learning predictive models during repeated internal 10-fold cross-validation
ModelAUC (mean) (maximum)TPR (mean) (maximum)FPR (mean) (minimum)Misclassification Error (mean) (minimum)
BG0.84 (0.91)0.75 (0.84)0.23 (0.18)0.23 (0.14)
RF0.84 (0.92)0.78 (0.89)0.25 (0.17)0.25 (0.16)
SVM0.85 (0.90)0.82 (0.95)0.26 (0.16)0.23 (0.17)
KNN0.82 (0.90)0.74 (0.90)0.23 (0.14)0.23 (0.15)
LR0.83 (0.92)0.73 (0.95)0.23 (0.15)0.24 (0.16)
  • Note:—AUC indicates area under the curve; BG, bagging or bootstrap aggregating; FPR, false-positive rate; KNN, K-nearest neighbor; LR, logistic regression; RF, random forest; SVM, support vector machine; TPR, true-positive rate.