Atrial Fibrillation Identification Using CNNs Based on Genomic Data

Atrial fibrillation (AF) is the most common cardiac arrhythmia and a major cardiovascular disease epidemic of the 21st century. Early diagnosis and intervention are crucial as AF often progresses without symptoms. This study aims to identify AF using genome-wide association studies and convolutional neural networks (CNN). Genomic data from 6,358 individuals were used to develop a CNN model, with L2 regularization applied to prevent overfitting. The L2-regularized CNN significantly outperformed the regular CNN across various p-value thresholds. For instance, at p