Fig. 3: 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 training cohort. | npj Digital Medicine

Fig. 3: 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 training cohort.

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

Fig. 3: 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 training 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, and decision tree, and statistically similar to XGBoost model, but significantly lower than RF 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.

Back to article page