Heart Disease Detection

Heart disease detection research focuses on developing accurate and efficient methods for diagnosing cardiovascular conditions using various physiological signals, primarily electrocardiograms (ECGs) and heart sounds. Current efforts concentrate on improving model performance through advanced machine learning techniques, including deep learning architectures like vision transformers and recurrent neural networks, and addressing challenges like data imbalance and the need for explainable AI. These advancements aim to improve early diagnosis, personalize treatment strategies, and ultimately reduce morbidity and mortality associated with heart disease, impacting both clinical practice and public health.