Application of an end-to-end model with self-attention mechanism in cardiac disease prediction.

Heart disease is a prevalent global health challenge, necessitating early detection for improved patient outcomes. This study aims to develop an innovative heart disease prediction method using end-to-end deep learning, integrating self-attention mechanisms and generative adversarial networks to enhance predictive accuracy and efficiency in healthcare. We constructed an end-to-end model capable of processing diverse cardiac health data, including electrocardiograms, clinical data, and medical images.