Risk prediction of early esophageal varices in patients with liver cirrhosis based on interpretable machine learning
10.13406/j.cnki.cyxb.003631
- VernacularTitle:基于可解释机器学习的肝硬化患者早期并发食管静脉曲张风险预测研究
- Author:
Yuheng YIN
1
;
Yuwen WANG
;
Jie FAN
;
Chun YANG
;
Wei WANG
Author Information
1. 重庆医科大学公共卫生学院,重庆 400016
- Keywords:
machine learning;
liver cirrhosis;
esophageal varices;
risk factors
- From:
Journal of Chongqing Medical University
2025;50(3):389-396
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the risk factors for esophageal varices in patients with liver cirrhosis,to establish a predictive model,and to provide reasonable guidance for the prevention of early esophageal varices in patients with liver cirrhosis.Methods:A retrospective analysis was performed for 1 113 patients with liver cirrhosis who attended the hospitals in Chongqing,China from Decem-ber 2006 to May 2021.Recursive feature elimination(RFE)and four machine learning methods were used for the screening of features,and five machine learning predictive models were established by logistic regression,random forest,support vector machine(SVM),de-cision tree,and eXtreme Gradient Boosting(XGBoost).The receiver operating characteristic(ROC)curve was used to evaluate the per-formance of each model,and the model with the best performance was used to investigate the risk factors for esophageal varices in pa-tients with liver cirrhosis.SHAP plots were used to explain the impact of each risk factor on patients.Results:The XGBoost model showed the best performance in predicting the risk of esophageal varices in patients with liver cirrhosis,with an area under the ROC curve of 0.872(95%CI=0.813-0.918).SHAP plots showed that platelet count,diameter of the portal vein,cholinesterase,albumin,ala-nine aminotransferase,hemoglobin,prothrombin ratio,prothrombin time,and serum total protein were risk factors for esophageal vari-ces in patients with liver cirrhosis.Conclusion:This study shows that the XGBoost predictive model has a relatively high predictive value,and the risk factors obtained by this model have a certain guiding significance for the clinical prevention and treatment of early esophageal varices in patients with liver cirrhosis.
