Abstract
Electrocardiography (ECG) is a practical and cost-effective clinical tool. Machine learning may enable identification of novel ECG variables for predicting incident atrial fibrillation (AF). We included 3151 participants from MESA with 568 baseline ECG variables each in addition to demographic,
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