Heart Disease Prediction Using Retinal Images: A Deep Learning Approach

• Author(s): Ibrahim Khaleelulla Khan; Balaji TS; Gowtham R; Hruthik M; Kushal D
• Paper ID: 1712358
• Page: 1926-1929
• Published Date: 25-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

Heart disease represents a significant public health problem worldwide, and early discovery is required in order to improve clinical outcomes and decrease mortality. Retinal imaging can provide a noninvasive methodology for evaluating microvascular health, with strong associations to cardiovascular disease. This work introduces a simple deeplearning method to predict heart disease from retinal fundus images. The proposed system consists of preprocessing retinal fundus images, then extracting features with convolutional neural networks (CNN), and finally predicting heart disease using binary classification. The model displays strong predictive capabilities, demonstrating that retinal vascular patterns contain important cardiovascular risk biomarkers. The results suggest that retinal images could be an effective, scalable screening tool for early detection of heart disease.

Keywords

Deep Learning, Retinal Imaging, Heart Disease Prediction, Fundus Photography, Convolutional Neural Networks.

Citations

IRE Journals:
Ibrahim Khaleelulla Khan, Balaji TS, Gowtham R, Hruthik M, Kushal D "Heart Disease Prediction Using Retinal Images: A Deep Learning Approach" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 1926-1929 https://doi.org/10.64388/IREV9I5-1712358

IEEE:
Ibrahim Khaleelulla Khan, Balaji TS, Gowtham R, Hruthik M, Kushal D "Heart Disease Prediction Using Retinal Images: A Deep Learning Approach" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712358