Machine learning effectively predicts recurrent extrahepatic bile duct stones after common bile duct exploration, enhancing surgical decision-making.

• Random forest model achieved AUCs of 97.99% in training and 83.1% externally, outperforming other methods.
• Key risk factors include maximum stone diameter, common bile duct diameter, and direct bilirubin, with larger stones (>15 mm) significantly increasing recurrence risk.

Surgeons can use these insights for personalized patient assessments and interventions to mitigate postoperative complications.

• Leveraging SHAP analysis clarifies interactions between risk factors, enabling targeted prevention strategies.