Retina-Cardiac Screening: Risk Prediction of Heart Attack using Retinal Eye Images

Heart disease remains one of the most prevalent causes of death worldwide, with heart attacks accounting for a large proportion of morbidity and mortality. Early detection and accurate risk prediction are essential for prevention and timely medical intervention. Traditional diagnostic approaches often depend on invasive procedures or detailed clinical records, which may not always be practical or available. This research presents a novel, non-invasive approach for heart attack risk prediction using retinal fundus images. The retinal vasculature serves as a strong indicator of systemic cardiovascular health, and features such as vessel morphology, arteriovenous ratios, and tortuosity patterns are extracted for analysis. Conventional systems typically apply machine learning techniques such as AdaBoost and Convolutional Neural Networks (CNNs), relying on tabular medical datasets that include parameters like age, cholesterol, and blood pressure. Although effective, these methods do not fully exploit the rich visual biomarkers present in retinal images. The proposed system employs a deep learning model based on Recurrent Neural Networks (RNNs). High-resolution retinal images are transformed into sequential feature vectors representing vascular patterns across spatial regions. The RNN is designed to capture both spatial and temporal dependencies within these features, leading to more robust predictions of heart attack risk. Experimental results on publicly available retinal image datasets demonstrate that the RNN-based framework achieves an overall accuracy of 98%. These results outperform traditional machine learning classifiers, establishing the superiority of deep learning-based retinal analysis for cardiovascular risk prediction