Heart Disease Prediction Based on Retinopathy using Machine Learning
- DOI
- 10.2991/978-94-6463-852-3_35How to use a DOI?
- Keywords
- Heart Disease Prediction; Machine Learning; Convolutional Neural Networks; Cardiovascular Risk; Retinal Fundus Images; Early Diagnosis
- Abstract
Heart disease remains a leading cause of mortality globally, and early detection and preventive measures can help reduce the mortality rate. Recent studies suggest a correlation between retinopathy and cardiovascular diseases, highlighting the potential for using retinal images as a diagnostic tool for heart disease prediction. Our paper presents a comprehensive approach to developing a model that will detect heart disease by analyzing retinal fundus images through machine learning and by reviewing existing models. We have used various algorithms of Convolutional Neural Networks (CNN), which extract relevant features from the images and are used to predict the existence of heart disease. Also, indicate the challenges with existing models and the work done to predict cardiovascular risk. This study underscores the importance of integrating ophthalmic assessments into routine cardiovascular evaluations, potentially improving patient outcomes through timely intervention.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Aniket Dubey AU - Amarsinh Vidhate AU - Puja Padiya PY - 2025 DA - 2025/10/07 TI - Heart Disease Prediction Based on Retinopathy using Machine Learning BT - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025) PB - Atlantis Press SP - 560 EP - 573 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-852-3_35 DO - 10.2991/978-94-6463-852-3_35 ID - Dubey2025 ER -