Development and implementation of explainable AI-based machine learning models for predicting hospital stay and treatment costs in cardiovascular patients

Cardiovascular disease (CVD) remains the leading cause of global morbidity and mortality, with substantial economic implications for healthcare systems. Among hospitalized CVD patients, procedures such as angioplasty and coronary artery bypass grafting (CABG) are associated with prolonged lengths of stay (LOS) and elevated treatment costs, underscoring the need for robust predictive tools to support clinical and administrative decision-making. Therefore, this study aimed to develop and validate machine learning (ML) models to predict hospital LOS and treatment costs for cardiovascular inpatients using real-world clinical data. This applied, retrospective predictive modeling study was conducted in 2024 at specialized cardiovascular clinic of a tertiary teaching hospital in Tehran, Iran. A cohort of 7685 adult inpatients who underwent angioplasty or CABG between 2022 and 2023 was analyzed. Eight regression-based ML algorithms were developed to predict four outcomes: hospital LOS, patient out-of-pocket (OOP), insurer payment, and total treatment cost. Model performance was evaluated using the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). SHAP was applied to the best-performing model to enable both global and local interpretability. Finally, a dual-platform clinical decision support application (web-based and desktop) was developed. XGBoost consistently outperformed other models across all prediction tasks. On the test set, it achieved R² values of 0.7802 for LOS, 0.8473 for patient OOP, 0.8946 for insurer payment, and 0.6437 for total cost. SHAP analysis revealed LOS, intervention type, age, and comorbidities as key predictors. The deployed application demonstrated real-time utility in generating personalized predictions based on patient characteristics. This study presents a comprehensive, explainable, and clinically implementable ML framework for predicting LOS and treatment costs in cardiovascular care. By integrating high-performing models with explainable AI and real-world application, this approach offers a scalable solution for enhancing hospital resource planning and optimizing patient outcomes. Future work should focus on external validation of the models across multiple hospitals and healthcare systems to enhance their generalizability. Additionally, integrating broader clinical and socioeconomic variables may further improve the predictive performance and expand the applicability of the developed decision support tool.