Heart Attack Prediction Using Retinal Images

Abstract

Heart attack remains a leading cause of mortality worldwide, highlighting the critical need for early and reliable risk prediction methods. Recent advancements in medical imaging and machine learning have enabled innovative approaches to cardiovascular risk assessment. This project focuses on predicting heart attack risk using retinal eye images, leveraging the strong correlation between retinal vascular patterns and cardiovascular health. The retina, being a non-invasive window to the vascular system, provides crucial biomarkers such as vessel diameter, tortuosity, and occlusions, which are indicative of heart health. By employing advanced image processing techniques and deep learning algorithms, the system extracts and analyzes these features to accurately predict the risk of a heart attack. This approach offers a cost-effective, non-invasive, and efficient alternative to traditional diagnostic methods, aiming to support early intervention and personalized treatment plans. The proposed solution has the potential to revolutionize preventive healthcare and reduce the global burden of cardiovascular diseases.