A Review on Prediction of Diabetic Retinopathy Using Data Mining Algorithms

Authors

  • Kajal Sanjay Kothare  M-Tech, Department of Computer Science and Engineering, Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India
  • Prof. Kalpana Malpe  Assistant Professor Department of Computer Science and Technology, Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India

Keywords:

Data Mining, Artificial neural fuzzy interference system, K-Nearest-Neighbor (KNN), Machine Learning (ML), Support Vector Machines, Decision Trees

Abstract

The risking components of diabetic retinopathy (DR) were examined broadly in the past investigations, yet it stays obscure which chance variables were more connected with the DR than others. On the off chance that we can distinguish the DR related hazard factors all the more precisely, we would then be able to practice early avoidance systems for diabetic retinopathy in the most high-chance populace. The motivation behind this examination to study and consider the different predicting mechanisms for the DR in diabetes mellitus utilizing data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-02-28

Issue

Volume 5, Issue 1, January-February-2019

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Research Articles

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How to Cite

[1]
Kajal Sanjay Kothare, Prof. Kalpana Malpe, " A Review on Prediction of Diabetic Retinopathy Using Data Mining Algorithms" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.266-272, January-February-2019.