Abstract
Heart disease increases the mortality rate in the recent years across the world. So it is necessary to develop a model to predict the heart disease occurrence as early as possible with higher rate of accuracy. In this study, the cardiovascular disease is predicted by non-invasive method with the retinal image data. In this system, the retinal fundus image data are used to predict the heart disease occurrence. Cardiovascular disease can be detected from the changes in microvasculature, which is imaged from retina. The prediction of a disease is by considering features like age, gender, smoking status, systolic blood pressure, diastolic blood pressure, and HbA1c. Risk factors for heart disease occurrence are detected from the microvasculature of segmented retinal fundus image using MATLAB. The main objective of the proposed system is to predict the occurrence of heart disease from retinal fundus image with higher rate of accuracy.
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Abbreviations
- ANN:
-
Artificial Neural Network
- AVR:
-
Arteries and Veins Ratio
- CRAE:
-
Central Retinal Artery Equivalent
- CRVE:
-
Central Retinal Vein Equivalent
- CVD:
-
Cardiovascular Disease
- HD:
-
Heart Disease
- SVM:
-
Support Vector Machine
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Rekha, R., Brintha, V.P., Anushree, P. (2020). Heart Disease Prediction Using Retinal Fundus Image. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_71
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DOI: https://doi.org/10.1007/978-3-030-24051-6_71
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