Prediction of Risk of Unplanned Readmission or Death in Elderly Heart Failure Patients During the Vulnerable Phase: Machine Learning With SHAP Interpretation.

Heart failure (HF) as the final stage of cardiovascular disease in the elderly leads to frequent readmissions and seriously affects their quality of life. This study aimed to develop a predictive model for hospital readmission or death during the vulnerable phase in elderly HF patients, and to identify the key associated risk factors. The dataset was randomly divided into 70% training sets and 30% validation sets. Three feature selection methods were applied to the training data, followed by the construction of 18 predictive models using six machine learning (ML) algorithms (XGBoost, LightGBM, AdaBoost, GBDT, GNB, and SVM). The performance of each model was assessed on the validation set using receiver operating characteristic (ROC) curves, sensitivity, accuracy, specificity, F1 score, and Brier score. SHapley Additive exPlanations (SHAP) were used to interpret the feature contributions both globally and locally. Eleven models achieved an area under the ROC curve (AUC) greater than 0.8, with the Boruta-XGBoost model performing best, showing an AUC of 0.873 in the validation set, along with a sensitivity of 0.839, accuracy of 0.769, specificity of 0.747, F1 score of 0.634, and Brier score of 0.130. SHAP analysis revealed that the top five important features were hemoglobin (HGB), serum free thyroxine (FT4), age, diabetes, and serum potassium (K). The Boruta-XGBoost based risk prediction model, combined with SHAP interpretation, demonstrated high predictive accuracy and robust interpretability for forecasting hospital readmission or death during the vulnerable phase in elderly HF patients.