International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
FUSION OF RETINAL IMAGING AND CARDIO VASCULAR SIGNALS USING DEEP LEARNING FOR HEART ATTACK RISK ASSESSMENT Dr. R. Muruganantham1, Ch.sathwik2, G.sherishma 3, K.Arjun4, K.madhu 5 1Assistant Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India
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Abstract - Healthcare systems are increasingly integrating
are therefore essential to reduce complications and improve patient outcomes.
artificial intelligence (AI) and machine learning (ML) techniques to improve early disease detection, risk prediction, and patient management. Traditional approaches for diagnosing diabetic retinopathy (DR) and assessing cardiovascular risks remain largely manual, time-consuming, and prone to inaccuracies, particularly in resource-limited regions. This project proposes a smart healthcare platform developed using Django, which integrates secure user registration with admin-controlled activation, role-based access, and OTP-enabled password recovery to ensure data privacy. The system employs a pre-trained Efficient Net deep learning model for automated classification of DR severity from retinal images, thereby reducing reliance on manual interpretation. Furthermore, a fusion-based machine learning model (XG Boost) is implemented to predict heart attack risk by combining DR indicators with clinical parameters such as blood pressure, cholesterol, heart rate, and ECG scores. The proposed system not only provides risk classification into high, medium, or low categories but also generates probability scores to enhance personalized healthcare recommendations. By unifying deep learning image analysis with structured clinical data in a web-based application, the system enables early diagnosis, preventive care, and improved clinical decision-making. This integration demonstrates the potential of AI-driven healthcare solutions in enhancing accessibility, accuracy, and scalability in modern medical practice.
Traditional healthcare systems rely heavily on manual diagnosis, clinical risk calculators, and physician expertise. While effective, these methods are often time-consuming, resource-intensive, and prone to human error. Moreover, they typically fail to integrate multiple health indicators, thereby limiting the accuracy of risk predictions. With the rapid advancements in deep learning, image processing, and predictive analytics, there is an increasing need for automated systems that can assist clinicians and patients in early disease detection and personalized healthcare management. The proposed system addresses these challenges by developing a Django-based web platform that integrates secure user management with advanced AI-driven diagnostics. Using Efficient Net, the system automatically classifies retinal images to determine the severity of diabetic retinopathy, reducing reliance on manual interpretation. Additionally, a fusion-based XG Boost model combines retinal disease severity with clinical parameters such as blood pressure; cholesterol, heart rate, and ECG score to predict heart attack risk levels. The platform also incorporates role-based authentication, admin approval, and OTP-based password recovery, ensuring secure and reliable user access.
Key Words: Retinal Imaging, Cardiovascular Signals, Deep Learning, Multimodal Data Fusion, Heart Attack Risk Assessment, Cardiovascular Disease Prediction, Convolutional Neural Networks.
By combining deep learning image classification and structured clinical data analysis, this system provides a comprehensive solution for early diagnosis, preventive care, and intelligent decision support, thereby bridging the gap between traditional healthcare practices and modern AI driven approaches.
1. INTRODUCTION In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into healthcare has gained significant attention due to their potential to improve disease diagnosis, risk assessment, and patient care. Chronic diseases such as diabetic retinopathy (DR) and cardiovascular disorders remain among the leading causes of blindness and mortality worldwide. According to the World Health Organization (WHO), diabetic retinopathy affects nearly one-third of diabetic patients, while cardiovascular diseases account for a substantial percentage of global deaths annually. Early detection and preventive measures
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1.1 Role of Retinal Imaging in Cardiovascular Risk Analysis The human retina provides a unique, non-invasive window into the body’s microvascular system. Changes in retinal blood vessels—such as variations in vessel diameter, tortuosity, and the presence of microaneurysms—are closely associated with systemic cardiovascular conditions. Several clinical studies have shown that retinal biomarkers correlate strongly with hypertension, atherosclerosis, and coronary artery disease, which are major precursors to heart attacks. With advances in medical imaging and artificial intelligence,
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