Sudden Cardiac Arrest (SCA) is the leading cause of death among athletes of all age levels worldwide. Current prescreening methods for cardiac risk factors are largely ineffective, and implementing the International Olympic Committee recommendation for 12-lead ECG screening remains prohibitively expensive. To address these challenges, a preliminary comprehensive screening system (CSS) was developed to efficiently and economically screen large populations for risk factors to SCA. A protocol was established to measure a 4-lead ECG using an Apple Watch. Additionally, two key advances were introduced and validated: 1) A decomposition regression model to upscale 4-lead data to 12 leads, reducing ECG cost and usage complexity. 2) A deep learning model, the Transformer Auto-Encoder System (TAES), was designed to extract spatial and temporal features from the data for beat-based classification. TAES demonstrated an average sensitivity of 95.3% and specificity of 99.1% respectively in the testing dataset, outperforming human physicians in the same dataset (Se: 94%, Sp: 93%). Human subject trials (n = 30) validated the smartwatch protocol, with Bland-Altman analysis showing no statistical difference between the smartwatch vs. ECG protocol. Further validation of the complete CSS on a 20-subject cohort (10 affected, 10 controls) did not result in any misidentifications. This paper presents a mass screening system with the potential to achieve superior accuracy in high-throughput cardiac pre-participation evaluation compared to the clinical gold standard.