Risk prediction of early esophageal varices in patients with liver cirrhosis based on interpretable machine learning

Yin Yuheng, Wang Yuwen, Fan Jie, Yang Chun, Wang Wei

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Journal of Chongqing Medical University ›› 2025, Vol. 50 ›› Issue (03) : 389-396. DOI: 10.13406/j.cnki.cyxb.003631
Clinical research

Risk prediction of early esophageal varices in patients with liver cirrhosis based on interpretable machine learning

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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 December 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），decision tree，and eXtreme Gradient Boosting（XGBoost）. The receiver operating characteristic（ROC） curve was used to evaluate the performance of each model，and the model with the best performance was used to investigate the risk factors for esophageal varices in patients 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，alanine aminotransferase，hemoglobin，prothrombin ratio，prothrombin time，and serum total protein were risk factors for esophageal varices 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.

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machine learning / liver cirrhosis / esophageal varices / risk factors

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Yin Yuheng , Wang Yuwen , Fan Jie , et al . Risk prediction of early esophageal varices in patients with liver cirrhosis based on interpretable machine learning. Journal of Chongqing Medical University. 2025, 50(03): 389-396 https://doi.org/10.13406/j.cnki.cyxb.003631

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