Abstract
<title>Abstract</title> <p> <bold>Background and Objective:</bold> Diabetic patients with congestive heart failure (CHF) face a high risk of in-hospital mortality, yet existing prediction models lack specificity for this comorbidity and often suffer from poor interpretability. This study aimed to develop and validate an interpretable machine learning model for predicting in-hospital mortality in diabetic patients with CHF. <bold>Methods:</bold> Data were derived from the MIMIC-IV (derivation cohort, n=7,063) and eICU-CRD (external validation cohort, n=4,532) databases. Four feature selection methods (LASSO, Boruta, recursive feature elimination, and univariate logistic regression) identified shared predictors. Ten machine learning algorithms were benchmarked, and logistic regression was optimized. Model performance was assessed using ROC-AUC, PR-AUC, calibration, and decision curve analysis. SHAP provided global and local interpretability. An R Shiny web application was developed. <bold>Results:</bold> Thirteen independent predictors were selected: APS III, age, norepinephrine, vasopressin, anion gap, temperature, respiratory rate, intubation status, SOFA, mean corpuscular volume, phenylephrine, and dopamine. The logistic model achieved an ROC-AUC of 0.8663 (test set) and 0.751 (external set), with good calibration (Brier score 0.073–0.096) and net clinical benefit. SHAP revealed APS III as the most influential feature. The web-based tool enables real-time risk prediction with individualized explanations. <bold>Conclusion:</bold> This interpretable logistic model accurately predicts in-hospital mortality in diabetic patients with CHF, and the accompanying web application offers a transparent, user-friendly tool to support bedside risk stratification and clinical decision-making. </p>