Construction of a machine learning-based risk prediction model for acute stroke in maintenance hemodialysis patients

Background Acute stroke represents a significant complication in patients undergoing maintenance hemodialysis (MHD) and is associated with increased mortality rates in this population. This study aimed to construct a prediction model for acute stroke in these patients by using seven machine learning algorithms. Methods This study included 1,100 patients receiving MHD at the First Affiliated Hospital, Zhejiang University School of Medicine, between January 1, 2015, and December 31, 2024. The patients were divided into training ( n = 770) and test ( n = 330) sets at a 7:3 ratio. Following feature selection using least absolute shrinkage and selection operator (LASSO) regression, predictive models were developed using seven machine learning algorithms. Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and the Brier score. SHapley Additive exPlanations (SHAP) were used to quantify the contribution of individual features to model predictions. A web-based prediction tool was subsequently developed using the Shiny framework in R. Results The results showed that the extreme gradient boosting (XGBoost) model performed the best in the test set, with an AUC of 0.847 (95% confidence interval (CI) [0.791–0.902]), sensitivity = 0.73, specificity = 0.83, F1-score = 0.61 and a Brier score of 0.115. The SHAP analysis identified six most important predictors, which were ranked in the descending order as follows: pre-dialysis systolic blood pressure (SBP), serum albumin level, uric acid level, hemoglobin level, C-reactive protein level, and a history of coronary heart disease. Conclusions We successfully developed an interpretable XGBoost-based model that effectively predicts acute stroke risk in MHD patients.