Cardiovascular Disease Detection
Cardiovascular disease (CVD) detection research focuses on developing accurate and efficient diagnostic tools using various data sources, primarily aiming for early and reliable identification to improve patient outcomes. Current research heavily utilizes machine learning, particularly deep learning models like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs such as LSTMs), often combined with support vector machines (SVMs), applied to data from electrocardiograms (ECGs), chest X-rays, cardiac magnetic resonance imaging (CMRI), and even photoplethysmography (PPG) signals. These advancements offer the potential for improved diagnostic accuracy, reduced reliance on invasive procedures, and enhanced accessibility of CVD screening, particularly in resource-limited settings.