Abstract
Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Early detection and intervention are crucial for improving patient outcomes and reducing the burden on healthcare systems. Recent research suggests a potential link between retinal vascular changes and cardiovascular health. Retinal images offer a non-invasive means to assess microvascular abnormalities, making them an attractive source of data for predictive modeling. This project focuses on developing a machine learning model, specifically using Recurrent Neural Networks (RNNs), to analyze retinal images and detect patterns indicative of heart diseases. RNNs are well-suited for processing sequential data, making them suitable for capturing temporal dependencies in the retinal images and improving the predictive accuracy of the model.
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