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Table 2 Classification performance comparison between the DT and LR models

From: Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

Model Datasets Accuracy Sensitivity Specificity AUROC (95% Cl)
LR
  Model 1 Original imbalanced 0.901 0.418 0.971 0.694(0.684 ~ 0.704)
  Model 2 Random oversampling 0.839 0.854 0.837 0.846*(0.837 ~ 0.854)
  Model 3 Random undersampling 0.843 0.851 0.842 0.846*(0.838 ~ 0.854)
  Model 4 Hybrid sampling 0.839 0.856 0.837 0.847*(0.838 ~ 0.855)
  Model 5 SMOTE 0.838 0.855 0.836 0.846*(0.837 ~ 0.854)
DT
  Model 6 Original imbalance 0.915 0.588 0.962 0.775(0.766 ~ 0.785)
  Model 7 Random oversampling 0.874 0.942 0.864 0.903#(0.896 ~ 0.910)
  Model 8 Random undersampling 0.879 0.959 0.868 0.913#(0.907 ~ 0.919)
  Model 9 Hybrid sampling 0.873 0.926 0.866 0.896#(0.889 ~ 0.902)
  Model 10 SMOTE 0.851 0.920 0.841 0.880#(0.873 ~ 0.888)
  1. * P <0.05 compared with the AUROC value of Model 1. # P <0.05 compared with the AUROC value of Model 6.