Ensemble Machine Learning for Institutional-Level Hospital Mortality Prediction Using Clinical and Operational Indicators

Background: Accurate prediction of in-hospital mortality is essential for hospital-wide clinical decision-making and resource planning. Most machine learning frameworks rely on patient-level clinical data and focus on intensive care or disease-specific populations, while hospital operational metrics are rarely incorporated. Methods: We conducted a retrospective observational study using 36 months of institutional data from 2022 to 2024 obtained from a regional referral hospital. The dataset integrated clinical indicators with hospital operational metrics, including length of stay, bed occupancy rate, and bed turnover rate. Three machine learning models, Random Forest, XGBoost, and a feed-forward neural network, were developed alongside a linear regression baseline. A stacked ensemble approach was applied to capture nonlinear relationships. Model performance was evaluated using R ² , root mean squared error, and mean absolute error with five-fold cross-validation. Model interpretability was assessed using Shapley Additive exPlanations. Results: The stacked ensemble achieved the strongest predictive performance (R ²  = 0.84; RMSE = 4.49), while the neural network yielded the lowest MAE (2.74). Heart failure and cardiogenic shock emerged as influential clinical predictors. Although operational metrics showed limited direct effects, interaction terms improved model stability. Shapley analyses demonstrated consistent feature attributions across models, supporting interpretability. Conclusions: Integrating clinical severity indicators with hospital operational metrics using an explainable ensemble machine learning framework improves hospital-wide mortality prediction. Operational variables contribute modestly in isolation but enhance model robustness through interaction effects, highlighting the value of interpretable machine learning for institutional-level clinical decision support.