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
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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, and direct bilirubin as top risks, with nonlinearity (stones >15 mm 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 >15 mm elevating risk) are key synergistic risks. This study enables personalized clinical risk assessment and targeted interventions to reduce postoperative recurrence.
Source: Frontiers in Medicine
Authors: Yugang Cao, Xun Hu, Jun Guo, Tao Fang
Institutions: Hubei Polytechnic University, Huangshi Central Hospital
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4417263193