What question did this study set out to answer?

This study aims to develop and validate a machine learning-based classification model for identifying restless legs syndrome (RLS) in end-stage renal disease (ESRD) patients.

May 3, 2026Open Access

Machine learning for identification of restless legs syndrome-associated factors and classification model development in end-stage renal disease patients

Puntos clave

This study aims to develop and validate a machine learning-based classification model for identifying restless legs syndrome (RLS) in end-stage renal disease (ESRD) patients.
Enrolled 396 ESRD patients, randomly divided into training (70%) and testing (30%) sets.
Used machine learning algorithms including Logistic Regression, Random Forest, and SVM for classification.
Evaluated model performance using AUC-ROC, Brier score, and Decision Curve Analysis.
SVM achieved the best performance with an AUC of 0.791 (95% CI: 0.702–0.879) in the testing set.
SVM accuracy was 0.761, sensitivity 0.711, specificity 0.797, and F1-score 0.711.
SHAP analysis revealed β2-microglobulin and anemia were the most influential variables.

Resumen

Background Restless legs syndrome (RLS) is a common and debilitating complication in end-stage renal disease (ESRD) patients undergoing dialysis, significantly impairing sleep quality and quality of life. Screening of prevalent cases remains challenging. This study aimed to develop and validate an interpretable machine learning-based classification model for identifying RLS status in ESRD patients. Methods A total of 396 ESRD patients (173 hemodialysis, 223 peritoneal dialysis) were enrolled from April to October 2024. Patients were randomly divided into training (70%, n = 287) and testing (30%, n = 109) sets. Feature selection was performed using LASSO regression with five-fold cross-validation, followed by Akaike Information Criterion (AIC) refinement. Nine machine learning algorithms were developed: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), and Quadratic Discriminant Analysis (QDA). Model performance was evaluated using discrimination (AUC-ROC), calibration (Brier score, calibration curves), and clinical utility (Decision Curve Analysis, DCA). SHapley Additive exPlanations (SHAP) was employed to enhance model interpretability. Results Five variables were selected: β2-microglobulin, hemoglobin, diabetes mellitus, coronary heart disease, and alcohol consumption. SVM demonstrated optimal performance with AUC of 0.791 (95% CI: 0.702–0.879) in the testing set, outperforming other models. SVM achieved accuracy of 0.761, sensitivity of 0.711, specificity of 0.797, F1-score of 0.711, and Brier score of 0.183. Calibration curves showed good agreement between estimated and observed probabilities. DCA confirmed favorable net clinical benefit across threshold probabilities. SHAP analysis identified β2-microglobulin (mean |SHAP| = 0.131) and anemia as the most influential variables with diabetes, coronary heart disease, and alcohol consumption contributing moderately. SHAP dependence plots revealed interactions between β2-microglobulin and hemoglobin, as well as diabetes modifying the protective effect of higher hemoglobin. Conclusion We developed and validated an interpretable SVM-based classification model for identifying RLS in ESRD patients using readily available clinical variables. This model demonstrates promising performance and requires prospective external validation in multi-center cohorts before clinical implementation. This tool may facilitate screening of prevalent RLS cases and inform clinical decision-making.

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