Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12‐Lead ECGs Based on Left Atrial Remodeling

We hypothesized that analysis of serial ECGs could predict new-onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs immediately before AF occurrence. Our aim in this study was to compare the performance of 2 types of machine learning (ML) algorithms.