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Predictive Modeling Of Diabetic Retinopathy Disease By Use Of Machine Learning

According to a report by the World Health Organization (WHO), one of the leading causes of death by the end of 2030 will be diabetes, which is a serious disease. Timely treatment of this disease can prevent serious complications, including death. The number of people getting infected with diabetes is millions. The risk of getting this infection is common now a days and is more prevalent in women than men. Diagnosis process for diabetes is quite tedious. Diabetes retinopathy is a disorder that is caused by uncontrolled diabetes and can cause complete blindness if left untreated. Therefore, if detected early its treatment can prevent the unfavourable effects of diabetic retinopathy. The actual diagnosis of diabetes retinopathy by eye doctors takes a lot of time and patients need to suffer more during this time. Thus the latest achievements in science and technology makes it easier to predict the disease. The aim is to diagnose whether a person is diabetic or not using a phase-based machine learning method. This paper reviews, classifies and compares algorithms with previously suggested strategies to develop better and more efficient algorithms.