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Table 5 Performance comparisons of AUC for the survival prediction in both models

From: Leveraging hybrid biomarkers in clinical endpoint prediction

Classifiers CBM (AUC) IBM (AUC) IDI P-value
 Logistic Regression 0.75 0.93 0.47 0.002*
Random Forest 0.61 0.83 0.54 < 0.001*
Support Vector Machine 0.74 0.92 0.50 < 0.001*
Artificial Neural Network 0.59 0.82 0.52 < 0.001*
  1. * representing significance data