ArticleEuropean journal of medical research2025
Machine learning-based risk prediction model for arteriovenous fistula stenosis.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Computational Frontiers in Arteriovenous Fistula Maturation: A Review of Fluid Dynamics and Machine Learning Models.Journal of the American Society of Nephrology : JASN · 2026Review
- Review
- Acoustic-based Stenosis Detection for Dialysis Patients using Explainable Machine Learning.Research square · 2026Article
- An interpretable XGBoost model for predicting arteriovenous fistula dysfunction in end stage renal disease.Frontiers in surgery · 2026Article
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
- Application of Cluster Analysis Based on SHAP Values in Hemodialysis Patients Using Arteriovenous Fistula.International journal of general medicine · 2025Article
- Risk Prediction of Arteriovenous Fistula Dysfunction in Hemodialysis Patients Using Routine Clinical Indicators.Kidney diseases (Basel, Switzerland)Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundArteriovenous 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.
methodsClinical 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.
resultsXGBoost 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.
conclusionThe XGBoost model effectively predicts arteriovenous fistula stenosis risk using clinical data. SHAP explanations enhance clinical interpretability, aiding personalized care strategies.
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