An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study.

Background: Gangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an explainable and cost-effective predictive model for GC utilizing ML and Shapley Additive explanation (SHAP) algorithm.

Results: This study included a total of 1006 patients with 26 clinical features. Through 5-fold CV, the best performing integrated learning model, XGBoost, was identified. The model was interpreted using SHAP to derive the feature subsets WBC, NLR, D-dimer, Gallbladder width, Fibrinogen, Gallbladder wallness, Hypokalemia or hyponatremia, these subsets comprised the final diagnostic prediction model.

Conclusions: The study developed a explainable predictive tool for GC at an early stage. This could assist doctors to make quick surgical intervention decisions and perform surgery on patients with GC as soon as possible.