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Table 3 Comparisons of predictive accuracies among random forest, logistic regression, SVC, and KNN models for adverse outcomes of ED patients with chest pain

From: Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain

Outcomes and predictive models Accuracy Precision Sensitivity Specificity F1 AUC
AMI < 1 month
 Random forest 0.915 0.916 0.915 0.882 0.915 0.915
 Logistic regression 0.868 0.885 0.868 0.766 0.867 0.868
 SVC 0.631 0.635 0.631 0.538 0.627 0.631
 KNN 0.865 0.880 0.865 0.766 0.864 0.865
All-cause mortality < 1 month
 Random forest 0.999 0.999 0.999 1.000 0.999 0.999
 Logistic regression 0.716 0.717 0.716 0.690 0.716 0.716
 SVC 0.656 0.660 0.656 0.584 0.654 0.656
 KNN 0.969 0.971 0.969 0.940 0.969 0.969
  1. SVC support-vector clustering; KNN K-nearest neighbors; ED emergency department; F1 2 x (precision x recall/precision + recall); AUC area under the curve; AMI acute myocardial infarction