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Table 2 Comparisons of predictive accuracies among random forest, logistic regression, KNN, SVM, LightGBM, MLP, and XGBoost in the outcomes of testing dataset of older ED patients with influenza

From: Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

Outcomes and predictive models Accuracy Sensitivity Specificity PPV NPV AUC
Hospitalization
 Random forest 0.769 0.744 0.791 0.762 0.775 0.840
 Logistic regression 0.737 0.751 0.726 0.711 0.764 0.799
 KNN 0.736 0.737 0.736 0.715 0.757 0.790
 SVM 0.750 0.751 0.749 0.728 0.770 0.840
 LightGBM 0.748 0.714 0.780 0.744 0.752 0.823
 MLP 0.733 0.702 0.760 0.724 0.740 0.806
 XGBoost 0.721 0.705 0.736 0.706 0.735 0.800
Pneumonia
 Random forest 0.679 0.681 0.679 0.562 0.778 0.765
 Logistic regression 0.662 0.661 0.662 0.542 0.764 0.709
 KNN 0.645 0.700 0.613 0.522 0.771 0.683
 SVM 0.657 0.700 0.631 0.534 0.777 0.733
 LightGBM 0.653 0.700 0.625 0.530 0.775 0.724
 MLP 0.660 0.660 0.660 0.540 0.762 0.688
 XGBoost 0.674 0.700 0.658 0.553 0.784 0.744
Sepsis or septic shock
 Random forest 0.795 0.750 0.798 0.179 0.982 0.857
 Logistic regression 0.799 0.750 0.801 0.182 0.982 0.832
 KNN 0.714 0.750 0.712 0.133 0.980 0.785
 SVM 0.707 0.750 0.705 0.130 0.980 0.806
 LightGBM 0.739 0.739 0.739 0.143 0.980 0.822
 MLP 0.730 0.728 0.730 0.137 0.979 0.761
 XGBoost 0.744 0.739 0.744 0.146 0.980 0.811
ICU admission
 Random forest 0.860 0.722 0.862 0.054 0.996 0.885
 Logistic regression 0.720 0.778 0.719 0.030 0.997 0.867
 KNN 0.607 0.611 0.607 0.017 0.993 0.622
 SVM 0.768 0.778 0.768 0.036 0.997 0.778
 LightGBM 0.809 0.722 0.810 0.040 0.996 0.874
 MLP 0.629 0.611 0.629 0.018 0.993 0.649
 XGBoost 0.912 0.722 0.914 0.085 0.997 0.902
In-hospital mortality
 Random forest 0.792 0.806 0.792 0.079 0.995 0.875
 Logistic regression 0.816 0.806 0.816 0.089 0.995 0.889
 KNN 0.652 0.639 0.652 0.039 0.988 0.663
 SVM 0.789 0.722 0.791 0.071 0.992 0.762
 LightGBM 0.769 0.722 0.770 0.065 0.992 0.844
 MLP 0.675 0.667 0.675 0.044 0.989 0.728
 XGBoost 0.751 0.806 0.750 0.067 0.994 0.858
  1. KNN K-nearest neighbors; SVM Support vector machine; LightGBM Light gradient boosting machine; MLP Multilayer perceptron, XGBoost Extreme Gradient Boosting; ED Emergency department; PPV Positive predictive value; NPV Negative predictive value; AUC Area under the curve