Exploring The Efficacy of Convolutional Neural Networks in Predicting Cardiovascular Disease Risk Factors from Biomedical Imaging Data

This exploration investigates the adequacy of convolutional neural networks (CNNs) in foreseeing cardiovascular disease (CVD) risk factors utilizing biomedical imaging information. Utilizing a different dataset including X-ray, CT, and ultrasound pictures close by clinical metadata, four CNN structures (CNN, Reset, Dense Net, and VGGNet) were executed and assessed. DenseNet arose as the top-performing model, accomplishing an exactness of 88.1%, accuracy of 90.1%, review of 86.2%, and F1 score of 88.0%. These outcomes show the prevalent prescient exhibition of Dense Net in contrast with different structures. The review adds to the progression of cardiovascular gamble evaluation techniques, featuring the capability of CNN-based approaches for further developing clinical independent direction and patient results. Pushing ahead, future examination bearings incorporate the investigation of bigger and more different datasets, as well as improving model interpretability and coordinating multimodal imaging information and clinical biomarkers.