BMC medical informatics and decision making
Development of a multimodel predictive framework for gangrenous cholecystitis with SHAP-based interpretability
By Niu QG, Yu Y, Xu F and 4 others.
Abstract
Background
Gangrenous Cholecystitis (GC) is a critical pathological type of acute cholecystitis that tends to induce gallbladder wall necrosis or perforation, thereby leading to localized or diffuse peritonitis and is associated with high morbidity and mortality. Due to its nonspecific clinical symptoms and strong heterogeneous manifestations, timely and accurate identification of GC preoperatively remains a clinical challenge. To address this unmet clinical need, we developed an interpretable machine learning (ML) model using routinely available preoperative variables to facilitate the early prediction of GC and support clinical decision-making.
Methods
This was a retrospective cohort study, including 487 patients with acute cholecystitis who underwent laparoscopic cholecystectomy in our hospital between February 2020 and July 2023. After initial univariate analysis and logistic regression, nine ML models were constructed, including logistic regression (LR), random forest (RF), XGBoost, LightGBM, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and a Stacking ensemble model. LASSO regression was used to select key preoperative predictors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, F1-score, sensitivity, specificity, decision curve analysis (DCA), and calibration curves. SHAP (SHapley Additive exPlanations) analysis was performed to enhance model interpretability.