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@article{176741,
        author = {Smita Wagh and Tejas Khatkale and Devashri Khairnar and Yash Mali and Sanika Najan},
        title = {Machine Learning-Driven Heart Disease Risk Prediction and Stratification from Retinal Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7665-7670},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176741},
        abstract = {Heart attacks and hypertension are major healthcare issues which affect microvascular structure and function adversely. Visualizing early pathological changes in retinal blood vessels becomes possible with non-catheter-based retinal fundus imaging. The study develops an AI-driven system using machine learning methods to diagnose cardiovascular diseases in preclinical stages by evaluating retinal vessel pictures. The research approach begins with retinal image collection followed by vascular segmentation methods which clean and enhance meaningful vascular features of the retina without superfluous structures. The trained deep learning models use separated features to identify situations of heart attacks and hypertension. The system intends to assist in prompt medical detection through its capacity to spot minimal vessel irregularities that usually escape human notice especially within younger groups. Through this system the correct detection and prediction of cardiovascular risks utilizes retinal image processing combined with morphological analysis and artificial intelligence. This medical approach demonstrates dual value in diagnostic precision within ophthalmology as well as cardiology and generates superior therapeutic designs and established clinical outcome measures.},
        keywords = {Cardiovascular Disease, Retinal Vessel Segmentation, Machine Learning, Hypertension Detection, Deep Learning in Healthcare, Retinal Image Analysis},
        month = {May},
        }