Predicting infected pancreatic necrosis in acute pancreatitis using machine learning models and feature selection

Infected pancreatic necrosis (IPN) is a life-threatening complication of acute pancreatitis (AP), and its early prediction remains challenging. This study aimed to develop and externally validate interpretable machine learning models for individualized IPN risk prediction. A total of 728 patients with AP admitted to Xuanwu Hospital, Capital Medical University, between 2017 and 2023 were retrospectively analyzed. Embedded feature selection was incorporated within model training using regularized linear and tree-based algorithms to enhance interpretability and prevent overfitting. Five machine learning algorithms and one neural network model were evaluated through nested cross-validation and an independent temporal external cohort consisting of 166 AP patients admitted to Xuanwu Hospital, Capital Medical University, between 2022 and 2023. Model discrimination, precision-recall, and probability calibration were assessed, and model explainability was analyzed using Shapley Additive Explanations (SHAP). The Random Forest model achieved the best overall performance, achieving an external AUC of 0.764 (95% CI 0.696-0.830, [Formula: see text]), precision of 0.893, recall of 0.604, and the lowest Brier score, indicating reliable probability calibration. SHAP analysis identified Fibrinogen, APACHE II score, D-dimer, IL-6, and C-reactive protein as key predictors associated with increased IPN risk, while higher Lymphocyte count, and Hematocrit were protective. These findings are consistent clinical pathophysiology. The interpretable Random Forest model demonstrated robust discrimination and calibration for IPN prediction, providing a transparent and data-driven framework for early risk stratification in acute pancreatitis. Prospective multicenter validation is warranted before clinical implementation.