Abstract: Objective  This study aimed to compare different machine learning algorithms to identify the optimal prediction model for the short-term mortality risk in patients undergoing maintenance hemodialysis (MHD), for uremia, providing a basis for early clinical risk stratification and intervention. Methods  A total of 197 uremic patients undergoing MHD at The Second Affiliated Hospital of Air Force Military Medical University from April 2022 to May 2024 were retrospectively enrolled. Based on 12-month survival outcomes, they were categorized into a death group (n=54) and a survival group (n=143). Using independent risk factors identified by multivariate logistic regression, five machine learning models—Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1 score, and accuracy. Decision Curve Analysis (DCA) was used to assesse clinical net benefit, and SHapley Additive exPlanations (SHAP) was applied to quantify the contribution of each clinical feature to the predictions. Results  Multivariate Logistic regression showed that diabetes (OR=14.347, 95%CI: 1.796～114.624, P=0.012), total cholesterol (OR=62.811, 95%CI: 2.463～1601.852, P=0.012), CRP (OR=4.723, 95% CI: 1.592-14.011, P=0.005) were independent risk factors for short-term mortality in patients with uremic MHD (P <0.05), while ALB (OR=-0.917, 95%CI: (0.231～0.693, P=0.001) was an independent protective factors (P<0.05). Comparison of the performance of the five algorithms reveals that the AUC value of the KNN model achieved the highest AUC value of 0.909, and an F1 score of 0.796, identifying it as the optimal predictive model for short-term mortality risk in uremic MHD patients. SHAP analysis identified ALB, CRP, diabetes, and total cholesterol as the most influential predictors. Conclusion: The KNN model integrates key indicators such as ALB, CRP, diabetes and total cholesterol, effectively predicts the short-term mortality risk in patients with uremic MHD.

Key words: Maintenance hemodialysis, Death, Machine learning, Predictive modeling