Cardiovascular Disease

Cardiovascular disease (CVD) research focuses on improving early detection and risk prediction to reduce mortality rates, a leading global health concern. Current efforts leverage machine learning, employing diverse algorithms like deep learning (convolutional neural networks, recurrent neural networks), ensemble methods, and support vector machines, often applied to data from ECGs, retinal imaging, and other medical scans. These models aim to improve diagnostic accuracy, personalize risk assessment, and potentially utilize readily available data sources like social media or even readily available imaging like retinal OCT scans. The ultimate goal is to enhance clinical decision-making and facilitate timely interventions.