INTELLIGENT HEART ATTACK RISK ASSESSMENT AND HEART DISEASE DETECTION FROMR ETINAL FUNDUS IMAGES USING AI AND DEEP LEARNING
DOI:
https://doi.org/10.5281/zenodo.21377043Keywords:
Heart Disease Detection, Retinal Fundus Imaging, Deep Learning, Efficient Net, Machine Learning, Risk AssessmentAbstract
This paper presents an intelligent healthcare system for early heart attack risk assessment and heart disease detection using retinal fundus images and artificial intelligence techniques. The proposed system integrates deep learning, computer vision, and machine learning to improve prediction accuracy. Retinal fundus images are used as a non-invasive diagnostic source, where vascular patterns are analyzed using convolutional neural networks and Efficient Net to extract meaningful features related to cardiovascular conditions.
A machine learning layer further enhances prediction by incorporating clinical parameters such as age, blood pressure, and cholesterol levels. The system also includes a generative AI-based doctor module that provides personalized explanations and preventive suggestions, improving user understanding and engagement. Additionally, an emergency alert system is integrated to notify high-risk conditions for timely intervention.
The proposed framework combines image analysis, structured data processing, and intelligent assistance to deliver a scalable and efficient solution for early cardiovascular disease detection, contributing to improved healthcare outcomes
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