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. 2024 Mar 15;110(6):3527–3535. doi: 10.1097/JS9.0000000000001276

Table 2.

Performance evaluation of five algorithm models in predicting postoperative in-hospital mortality.

Testing cohort
Metrics XGBoost RF LR SVM NB
AUC 0.8504 0.8961 0.7893 0.7705 0.8024
F1 0.1214 0.1284 0.0955 0.1235 0.0904
Se 0.8500 0.9500 0.7500 0.6500 0.8000
Sp 0.8508 0.8422 0.8287 0.8910 0.8047
Pr 0.0654 0.0688 0.0510 0.0682 0.0479
Acc 0.8508 0.8435 0.8277 0.8881 0.8047

Acc, Accuracy; AUC, Area Under the Curve; F1, F1 score; LR, Logistic Regression; NB, Naive Bayes; Pr, Precision; RF, Random Forest; Se, Sensitivity; Sp, Specificity; SVM, Support Vector Machine; XGBoost, eXtreme Gradient Boosting.