Fig. 5: ROC curves of ANN model, KNN model, decision tree model, RF model, XGBoost model, and LS-SVMR model for predicting the risk of in-hospital death in patients with cirrhosis and acute gastrointestinal bleeding in the validation cohort. | npj Digital Medicine

Fig. 5: ROC curves of ANN model, KNN model, decision tree model, RF model, XGBoost model, and LS-SVMR model for predicting the risk of in-hospital death in patients with cirrhosis and acute gastrointestinal bleeding in the validation cohort.

From: Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding

Fig. 5: ROC curves of ANN model, KNN model, decision tree model, RF model, XGBoost model, and LS-SVMR model for predicting the risk of in-hospital death in patients with cirrhosis and acute gastrointestinal bleeding in the validation cohort.The alternative text for this image may have been generated using AI.

a All patients; b Patients with variceal bleeding; c Patients who underwent endoscopic treatment; d Patients who received pharmacological treatment alone without endoscopic treatment. The performance of LS-SVMR model was significantly higher than ANN, KNN, decision tree, XGBoost, and RF models both in overall and subgroup analyses. ANN artificial neural network, KNN K-nearest neighbors, RF random forest, LS-SVMR least square support vector machine regression.

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