Machine learning-based risk prediction model for arteriovenous fistula stenosis.

Background: Arteriovenous fistula stenosis is a common complication in hemodialysis patients, yet effective predictive tools are lacking. This study aims to develop an interpretable machine learning model for stenosis risk prediction.

Methods: Clinical data from 974 patients (55 features) undergoing arteriovenous fistula dialysis at The Central Hospital of Wuhan (2017-2024) were analyzed retrospectively. The dataset was split into training (70%) and test (30%) sets. Seven models-Random Forest, XGBoost, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Network, and Decision Tree-were trained. Performance was evaluated using F1 score, accuracy, specificity, precision, recall, and AUC-ROC. SHAP values identified key predictors in the optimal model.

Results: XGBoost achieved the highest AUC (0.829, 95% CI 0.785-0.880). SHAP analysis highlighted seven critical predictors: number of surgeries, prothrombin time activity, lymphocyte count, fistula duration, triglycerides, vitamin B12, and C-reactive protein.

Conclusion: The XGBoost model effectively predicts arteriovenous fistula stenosis risk using clinical data. SHAP explanations enhance clinical interpretability, aiding personalized care strategies.