“Heart Attack Risk Prediction Using Retinal Eye Images Based On Machine Learning And Image Processing”

Abstract

Heart disease increases the mortality rate in recent years across the world. So, it is necessary to develop a model to predict heart disease occurrence as early as possible with a higher rate of accuracy. Till now the detections are gone through blood tests, ECGs, and invasive stress tests. In this project, heart disease is predicted by a non-invasive method with the retinal image data. A Chase image dataset is considered, as the health of our eyes is connected to the health of our heart. Here, Heart problems can be detected from the changes in the microvasculature, which is imaged from the retina. The prediction of disease is by considering features like the size of blood vessels, non-uniform background illumination, etc. We use Image processing for identifying patterns in images and the Support Vector Machine (SVM) and Random Forest Classifier (RFC) algorithm for classification. The main objective of the proposed system is to predict the occurrence of heart disease from retinal fundus images with a higher rate of accuracy.