PREDICTING THE RISK OF HEART ATTACK USING RETINAL EYE IMAGE ANALYSIS

Abstract: Globally, cardiovascular diseases (CVDs) continue to be the primary cause of morbidity and mortality. In order to enhance patient outcomes and lessen the strain on healthcare systems, early detection and intervention are essential. Changes in retinal vascular structure may have an impact on cardiovascular health, according to recent studies. Retinal pictures are a desirable source of data for predictive modelling because they provide a non-invasive way to evaluate micro vascular abnormalities. The goal of this project is to create a machine learning model that analyses retinal images and looks for patterns that could indicate heart illness. Specifically, this model uses Recurrent Neural Networks (RNNs). Because RNNs are good at processing sequential data, they can be used to better forecast the model and capture temporal dependencies in retinal pictures.