Authors : Rajani; Paavana Guruprasad; Rahul M Javkar; Rakshith C R; Rakshitha T S

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/54psremz

Scribd : https://tinyurl.com/3yja7dzp

DOI : https://doi.org/10.5281/zenodo.14730631

Abstract : With the rising incidence of heart attacks, early detection systems have become essential in providing timely intervention to reduce fatalities and complications. This project presents a non-invasive heart attack risk prediction system designed to analyze retinal images for identifying vascular patterns indicative of heart attack risks. By leveraging advanced artificial intelligence techniques, including Recurrent Neural Networks (RNNs) and Expectation-Maximization (EM) algorithms, the system detects subtle abnormalities in retinal blood vessels that correlate with cardiovascular health. Upon detection, the system provides real- time risk assessments through a web-based platform, enabling individuals and healthcare providers to take proactive measures. This innovative solution offers a reliable, efficient, and scalable approach to heart attack risk prediction, ensuring improved accessibility and enhanced preventive care.

Keywords : Retinal Imaging, Cardiovascular Diseases, Heart Attack Prediction, Artificial Intelligence, Non-Invasive Diagnostics, Recurrent Neural Networks, Expectation-Maximization.

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