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Average model performance by classifier typea
ModelLRSVMRFMLP
AUROC0.936 (0.910–0.959)0.932 (0.907–0.954)0.950 (0.931–0.967)0.887 (0.848–0.922)
AUPRC0.695 (0.610–0.779)0.650 (0.565–0.740)0.710 (0.626–0.792)0.613 (0.525–0.704)
Sensitivity0.748 (0.662–0.831)0.679 (0.589–0.768)0.728 (0.641–0.812)0.660 (0.573–0.747)
Specificity0.950 (0.935–0.964)0.944 (0.929–0.959)0.955 (0.940–0.968)0.938 (0.921–0.952)
Accuracy0.932 (0.916–0.947)0.923 (0.906–0.938)0.936 (0.921–0.951)0.914 (0.896–0.930)
  • Note:—LR indicates logistic regression; SVM, support vector machine; RF, random forest; MLP, multilayer perceptron.
  • ↵a Average AUROC, AUPRC, sensitivity, specificity, and accuracy are shown for each model type across all findings. Numbers in parentheses are the range of values corresponding to the 95% confidence interval