Sudden Cardiac Death (SCD) is a critical and unexpected condition that occurs due to cardiac causes within one hour of the onset of acute cardiovascular symptoms or twenty-four hours in unwitnessed cases. Despite advancements in cardiovascular medicine, practical methods for predicting SCD are still lacking, and there are no standardized systems to identify individuals at risk, especially in seemingly healthy populations such as athletes. In this study, we employed hierarchical clustering and principal component analysis (PCA) on data from 711 competitive athletes, revealing distinct patterns and cluster distributions in PCA space. Specifically, Clustering revealed characteristic feature combinations associated with increased SCD risk in athletes. Notably, certain clusters shared traits, including participation in Class C sports, sinus tachycardia, ventricular pre-excitation, personal or family history of heart disease, T-wave inversions, and prolonged QTc intervals. PCA helped visualize these patterns in distinct spatial regions, highlighting underlying structures and aiding intuitive risk interpretation. These results enable scientists to derive cluster metrics that serve as reference points for classifying new individuals and visually representing risk patterns in a clear graphical format. These findings establish a foundation for predictive tools that, with additional clinical validation, could aid in the prevention of SCD. The dataset used in this study, along with the clustering and PCA results, is available to the scientific community in an open format, together with the necessary tools and scripts to enable independent experimentation and further analysis.
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