Abstract
Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) is a leading contributor to sudden cardiac death worldwide in young adults, yet its diagnosis remains complex, expensive and time-consuming. Machine-learning (ML) classifiers offer a practical solution by delivering rapid, scalable predictions that can lessen dependence on expert interpretation and speed clinical decision-making. Here, we benchmarked six ML algorithms for ARVC detection using area-under-the-curve (AUC) and accuracy as primary metrics. Gradient Boosted Trees outperformed all other models, achieving a c-statistic of 94.34% after rigorous cross-validation. These results underscore the promise of Gradient Boosted Trees classifier as an effective decision-support tool within the ARVC diagnostic workflow, with potential to streamline evaluation and improve patient outcomes.