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Table 6 Results using 10-fold cross-validation for cardiovascular disease classification

From: A data-driven approach to predicting diabetes and cardiovascular disease with machine learning

Lab Year Model AUC Precision Recall F1
No lab   Logistic Reg. 0.822 0.74 0.74 0.74
  2007-2014 SVM 0.816 0.74 0.74 0.74
   Random Forest 0.829 0.75 0.74 0.74
   XGBoost 0.830 0.74 0.74 0.74
   Ensemble 0.831 0.75 0.75 0.75
With lab   Logistic Reg. 0.827 0.75 0.75 0.75
  2007-2014 SVM 0.825 0.75 0.75 0.75
   Random Forest 0.836 0.76 0.76 0.76
   XGBoost 0.838 0.76 0.76 0.76
   Ensemble 0.839 0.76 0.76 0.76
  1. Lab - Laboratory results, AUC - Area Under the Curve, \(Precision = \frac {TP}{TP + FP}, Recall = \frac {TP}{TP + FN}\) (where TP - True Positive, FP - False Positive, FN - False Negative), and F1 (score) = \(2\frac {precision*recall}{precision + recall}\). Bold face font signifies best performing model result