ORIGINAL RESEARCH article
Front. Med.
Sec. Hepatobiliary Diseases
Machine Learning-Based Prediction of Recurrent Extrahepatic Bile Duct Stones After Common Bile Duct Exploration: A Comparative Study of Models and SHAP-Driven Interpretability Analysis
Provisionally accepted
- Huangshi Central Hospital, Huangshi, China
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Purpose:This study aimed to construct and compare machine learning models for predicting recurrent extrahepatic bile duct stones after common bile duct exploration, and to clarify the contribution of key risk factors using SHAP analysis, thereby providing a reliable tool for clinical risk assessment and intervention. Methods:Retrospective analysis of 1,363 patients (2010–2024, Huangshi Central Hospital/Honghu People's Hospital) with extrahepatic bile duct stones (156 recurrent cases). LASSO regression selected 8 predictors; 9 machine learning models were built, evaluated by AUC, accuracy, etc., and SHAP interpreted the optimal model. Results:Random Forest (RF) performed best: training/validation/external cohort AUC 97.99%/93.66%/83.1%, accuracy 0.953/0.902/0.829. SHAP identified maximum stone diameter, common bile duct diameter, direct bilirubin as top risks, with nonlinearity (stones >15mm elevated risk) and synergistic interactions. Conclusion:Random Forest (RF) is confirmed as the most reliable tool for predicting recurrent extrahepatic bile duct stones post-common bile duct exploration, outperforming other models in generalization. SHAP analysis clarifies that max stone diameter, CBD diameter, and direct bilirubin (with nonlinear effects like stones >15mm elevating risk) are key synergistic risks. This study enables personalized clinical risk assessment and targeted interventions to reduce postoperative recurrence.
Keywords: machine learning, Recurrent Extrahepatic Bile Duct Stones, Common bile duct exploration, SHAP-Driven Interpretability Analysis, Key risk factors
Received: 23 Aug 2025; Accepted: 12 Nov 2025.
Copyright: © 2025 Cao, Hu, Guo and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tao Fang, 79704682@qq.com
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