Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding

Figure 2 Comparison of various machine learning models in a classification task and decision curve analysis for the logistic regression model. A: Receiver operating characteristic (ROC) curves for random forest, extreme gradient boosting, and logistic regression (LR); B: ROC curve for the LR model, presented individually with an area under the curve value of 0.825; C: Decision curve analysis for the LR model, illustrating the net benefit at various threshold probabilities in comparison to the “treat all” and “treat none” strategies. AUC: Area under the curve; XGB: Extreme gradient boosting; LR: Logistic regression.