- Cardiovascular disease is one of the most fatal conditions in the present world.
- In under a minute, an artificial intelligence program can take a picture of the back of a person’s eye and by analyzing the strength of the blood vessels that feed the retina find clues that may point to higher risks of a stroke or heart attack.
- A unifying goal of work like this is to develop new disease detection or monitoring approaches that are less invasive, more accurate, cheaper and more readily available.
- However, one restriction to potential broad population-level applicability of efforts to extract biomarkers from fundus photos is getting the fundus photos themselves, which requires specialized imaging equipment and a trained technician.
- Very few systems use the available clinical data for Classification purposes and even if they do ,they are restricted by the large number of association rules that apply.
- Diagnosis of the condition solely depends upon the Doctors’s intuition and patient’s records.
- Detection is not possible at an earlier stage.
- In the existing system, practical use of various collected data is time consuming .
- We have developed an artificial intelligence (AI) system that can analyze eye scans taken during a routine visit to an optician or eye clinic and identify patients at a high risk of a heart attack.
- Changes to the tiny blood vessels in the retina are indicators of broader vascular disease, including problems with the heart.
- The training data is trained by using proposed machine learning algorithm RNN classification clustering and Adaboost. algorithm is explained in detail.
- High performance and accuracy rate.
- RNN Classification is very flexible and is widely in various domains with high rates of success.
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