PREDICTING THE RISK OF HEART ATTACK USING RETINAL EYE IMAGE ANALYSIS

Abstract: Cardiovascular diseases (CVDs) remain the leading cause of death and illness worldwide. Early diagnosis and intervention are vital to improving patient outcomes and reducing the burden on healthcare systems. Recent research indicates that alterations in retinal vascular structure may be linked to cardiovascular health. Retinal images offer a non-invasive approach to assess microvascular anomalies, making them a valuable source of data for predictive modeling. This study aims to develop a machine learning model using Recurrent Neural Networks (RNNs) to analyze retinal images and detect patterns that could signal heart disease. RNNs are particularly well-suited for processing sequential data, enabling better predictions by capturing temporal dependencies in retinal images.