Cardiovascular Disease Risk

Cardiovascular disease (CVD) risk prediction is a crucial area of research aiming to improve early detection and intervention. Current efforts focus on developing and refining machine learning models, employing diverse architectures like deep learning networks (including transformers and convolutional neural networks), support vector machines, and ensemble methods, often leveraging multimodal data sources such as ECGs, retinal images, and clinical records. These advancements aim to enhance accuracy and interpretability, leading to more effective risk stratification and personalized preventative strategies. The ultimate goal is to improve patient outcomes through timely interventions and better risk communication.