Detecting pancreaticobiliary maljunction in pediatric congenital choledochal malformation patients using machine learning methods

By integrating preoperative clinical symptoms and laboratory parameters, machine learning algorithms demonstrated significant detection capability in identifying PBM among pediatric congenital choledochal malformation patients, with the RF model achieving superior performance metrics among all base models. The developed ensemble voting classifier provides valuable preoperative guidance for surgical planning and clinical management, enabling detection of PBM comorbidity before surgery in congenital choledochal malformation cases.