Knowledge based Expert System for Predicting Diabetic Retinopathy using Machine Learning Algorithms 
J.Jayashree1, Sruthi R2, Ponnamanda Venkata Sairam3, J.Vijayashree4
1J. Jayashree*, School of Computer Science and Engineering, VIT,Vellore
2Sruthi R, School of Computer Science and Engineering, VIT,Vellore Ponnamanda Venkata Sairam, School of Computer Science and Engineering, VIT,Vellore
3J. Vijayashree, School of Computer Science and Engineering, VIT,Vellor
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 19-27 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6397029302/2020©BEIESP | DOI: 10.35940/ijeat.C6397.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetic retinopathy (DR) is a medical condition that can affect the patient’s retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.
Keywords: Diabetic retinopathy, feature selection, classification, Complications, Treatment, Prevention, Statistics.