Explainable machine learning model integrating clinical and radiomic features for predicting acute suppurative cholecystitis

Figure 2 Workflow of model development. Radiomic features were extracted using Pyradiomics after manual segmentation. Feature selection involved t-test, Pearson correlation filtering, and least absolute shrinkage and selection operator regression. Radiomics and clinical models were constructed using logistic regression. Stacking strategy was used to integrate outputs from both models into a fusion model. WBC: White blood cells; GB: Gallbladder; STB: Serum total bilirubin; NE: Neutrophil granulocytes; UCB: Unconjugated bilirubin; LASSO: Least absolute shrinkage and selection operator; SHAP: SHapley Additive exPlanations.