Abstract
OBJECTIVES: Heart failure (HF) patients admitted to intensive care units are prone to early readmission, which leads to adverse outcomes and increased healthcare costs. Existing prediction models often suffer from data heterogeneity, class imbalance, and limited interpretability. This study aimed to develop an interpretable ensemble learning framework to predict 30-day ICU readmission in adult patients with HF and to compare its performance with conventional single-classifier approaches. METHODS: This retrospective study analyzed 5414 adult HF patients from the MIMIC-III database. Using clinical and demographic variables collected within the first 24 h of the index ICU admission, the study aimed to predict 30-day ICU readmission (return to ICU). A two-stage ensemble model was developed using stratified sampling and grid-search optimization, with top learners integrated via a soft-voting mechanism. Additionally, SHapley Additive exPlanation (SHAP) analysis was employed to ensure model interpretability and quantify variable contributions to the predictions. RESULTS: The KNN-imputed Voting (3 Models) ensemble emerged as the optimal framework, achieving an accuracy of 0.8413, F1-score of 0.8195, and AUROC of 0.6718. Despite moderate AUROC, the model achieved strong recall and reliable calibration, making it suitable for risk stratification in post-ICU care transitions. The SHAP analysis identified Glucose, hemodynamic parameters (e.g., blood pressure, heart rate), and inflammatory indicators as key predictors, aligning with established clinical understanding of stress hyperglycemia and hemodynamic instability in HF. CONCLUSION: This interpretable ensemble framework predicts 30-day ICU readmission in HF patients with robust performance, effectively balancing sensitivity and discrimination. It supports electronic health record-based risk stratification and timely intervention. Future work should focus on external validation across diverse populations to ensure generalizability.