Classification of "Athlete's Heart" using machine learning of conventional 12-lead ECG: male elite 3,000-m runner data in the CHIEF study.

Background: Conventional electrocardiographic (ECG) interpretive algorithms often struggle to distinguish physiological cardiovascular adaptations in athletes from cardiac pathology. This study utilized machine learning (ML) to estimate the likelihood of elite 3,000-meter running performance using resting ECG and biological markers within a large military cohort. Methods: We analyzed data from 2,296 physically active military males in the Cardiorespiratory Fitness and Health in Eastern Armed Forces (CHIEF) Heart Study. Three ML classifiers-logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM)-were trained using 26 ECG features (axis, duration and voltage of P, QRS and T waves in each lead and supine heart rate) and 6 biological features (age, body weight, body height, waist circumference, blood pressure and sitting pulse rate) to classify "elite" runners (defined as the top 5% and 10% performance groups). The whole data were randomly grouped by a 3:1 ratio into a training/validation set ( Results: For the top 10% runners, LR demonstrated the highest discriminative power (AUC: 80.81%), followed by MLP (78.43%). In the top 5% group, MLP performed best with an AUC of 74.42%. When specificity was fixed at approximately 60%-70%, the sensitivity of the optimal models for both groups exceeded 81%. Conclusions: ML can effectively classify elite endurance capacity using non-invasive resting ECG markers. These findings highlight the potential for updating automated ECG interpretive algorithms to better recognize "athlete's heart." Furthermore, this approach may serve as a cost-effective preliminary screening tool for identifying elite athletic potential, although further validation in female and more diverse populations is warranted.