A machine learning analysis to identify biomarkers on Holter data of white matter lesions in Fabry disease patients

Abstract Fabry disease (FD) is a rare genetic disorder associated with cardiac abnormalities and often overlooked brain white matter lesions (WMLs). Despite the importance of early WMLs detection, diagnosis is frequently delayed. The aim is to identify electrocardiographic biomarkers linked to WMLs in middle-aged FD patients using machine learning, assessing their potential as non-invasive diagnostic tools. This retrospective study analyzed electrocardiographic data from FD patients aged 40–59. A feature selection process based on variance inflation factor analysis identified nine relevant features, including heart rate variability and QT interval parameters. Machine learning classifiers—logistic regression, support vector machines, random forest, and k-nearest neighbors—were trained and evaluated using accuracy, sensitivity, specificity, and AUC. SHAP (SHapley Additive exPlanations) analysis was used to interpret model predictions. The random forest model achieved the highest accuracy (0.81) using all nine features. A subset consisting of SDANN 5 and QTc Min also performed well (accuracy 0.75) in other models. SHAP analysis highlighted SDANN 5 as a key predictor. Machine learning applied to ECG data shows promise for early WML detection in FD, supporting the integration of computational methods into diagnostics for complex genetic diseases.