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Table 4 Comparison of the prediction ability of each model ()

From: Early prediction of MODS interventions in the intensive care unit using machine learning

 

MIMIC-III

MIMIC-IV

Models

AUC

Accuracy

Sensitivity

Specificity

YI

Utility_score

AUC

Accuracy

Sensitivity

Specificity

YI

utility_score

Super learner

0.949 ± 0.014

0.902 ± 0.013

0.896 ± 0.014

0.930 ± 0.012

0.826 ± 0.011

0.780 ± 0.011

0.941 ± 0.014

0.893 ± 0.011

0.884 ± 0.011

0.929 ± 0.013

0.813 ± 0.014

0.763 ± 0.011

SubSuperLearner

0.942 ± 0.012

0.901 ± 0.014

0.896 ± 0.012

0.928 ± 0.014

0.824 ± 0.014

0.778 ± 0.012

0.935 ± 0.012

0.888 ± 0.012

0.880 ± 0.014

0.925 ± 0.014

0.809 ± 0.012

0.760 ± 0.011

DWNN

0.967 ± 0.012

0.891 ± 0.011

0.881 ± 0.011

0.939 ± 0.013

0.820 ± 0.012

0.705 ± 0.015

0.960 ± 0.013

0.882 ± 0.014

0.869 ± 0.012

0.935 ± 0.012

0.804 ± 0.011

0.690 ± 0.015

lightgbm

0.964 ± 0.015

0.887 ± 0.013

0.879 ± 0.012

0.927 ± 0.012

0.806 ± 0.012

0.759 ± 0.013

0.959 ± 0.014

0.884 ± 0.014

0.873 ± 0.012

0.930± 0.015

0.903 ± 0.012

0.738 ± 0.015

random forest

0.963 ± 0.014

0.886 ± 0.012

0.876 ± 0.012

0.932 ± 0.011

0.808 ± 0.015

0.734 ± 0.012

0.958 ± 0.012

0.878 ± 0.014

0.863 ± 0.015

0.943 ± 0.012

0.806 ± 0.012

0.711 ± 0.012

XGBoost

0.959 ± 0.014

0.887 ± 0.012

0.882 ± 0.012

0.910 ± 0.012

0.792 ± 0.013

0.756 ± 0.014

0.953 ± 0.012

0.878 ± 0.014

0.868 ± 0.013

0.921 ± 0.011

0.789 ± 0.012

0.730 ± 0.012

AdaBoosting

0.958 ± 0.014

0.873 ± 0.011

0.868 ± 0.012

0.897 ± 0.012

0.765 ± 0.012

0.726 ± 0.011

0.954 ± 0.013

0.867 ± 0.013

0.853 ± 0.012

0.928 ± 0.013

0.781 ± 0.012

0.705 ± 0.013

Logistic Regression

0.955 ± 0.014

0.883 ± 0.014

0.875 ± 0.012

0.919 ± 0.015

0.794 ± 0.015

0.678 ± 0.013

0.944 ± 0.013

0.872 ± 0.014

0.862 ± 0.015

0.919 ± 0.013

0.781 ± 0.012

0.657 ± 0.015

Naïve Bayes

0.938 ± 0.012

0.834 ± 0.013

0.815 ± 0.015

0.923 ± 0.011

0.738 ± 0.012

0.657 ± 0.014

0.936 ± 0.013

0.810 ± 0.014

0.779 ± 0.012

0.942 ± 0.012

0.721 ± 0.015

0.620 ± 0.012

KNN

0.938 ± 0.013

0.850 ± 0.013

0.833 ± 0.014

0.932 ± 0.012

0.765 ± 0.012

0.616 ± 0.011

0.920 ± 0.011

0.828 ± 0.012

0.804 ± 0.012

0.932 ± 0.015

0.736 ± 0.014

0.608 ± 0.013

Decision Tree

0.928 ± 0.012

0.852 ± 0.014

0.839 ± 0.011

0.912 ± 0.012

0.751 ± 0.015

0.667 ± 0.014

0.914 ± 0.011

0.823 ± 0.012

0.798 ± 0.015

0.926 ± 0.014

0.724 ± 0.011

0.610 ± 0.012