{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shu P"],"funding":["Chen Xiaoping Foundation for the development of science and technology of Hubei province"],"pagination":["217"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11954292"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["30(1)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>The XGBoost model effectively predicts arteriovenous fistula stenosis risk using clinical data. SHAP explanations enhance clinical interpretability, aiding personalized care strategies."],"journal":["European journal of medical research"],"pubmed_title":["Machine learning-based risk prediction model for arteriovenous fistula stenosis."],"pmcid":["PMC11954292"],"funding_grant_id":["CXPJJH124001-2404"],"pubmed_authors":["Qiu J","Shu P","Huo S","Xu F","Huang L","Wang X","Bai H"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning-based risk prediction model for arteriovenous fistula stenosis.","description":"<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>The XGBoost model effectively predicts arteriovenous fistula stenosis risk using clinical data. SHAP explanations enhance clinical interpretability, aiding personalized care strategies.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-07-13T03:04:06.816Z","creation":"2025-07-13T03:04:06.816Z"},"accession":"S-EPMC11954292","cross_references":{"pubmed":["40156016"],"doi":["10.1186/s40001-025-02490-x"]}}