Cardiovascular Risk Prediction System Using Retinal Images

Cardiovascular diseases (CVDs), especially heart attacks, are major health challenges worldwide. Reducing death rates requires prompt intervention and early discovery. This study introduces a non-invasive method for determining cardiovascular risk using retinal pictures. By employing a Convolutional Neural Network (CNN) based binary classifier, the system automates the risk assessment process by examining retinal features like blood vessel structures and anomalies. Integrated into a web-based graphical user interface, this system allows healthcare professionals to upload retinal images and obtain risk predictions. The suggested approach's remarkable 95% accuracy shows how retinal imaging and deep learning can be combined to help with early diagnosis and reduce the strain of medical professionals.