Machine Learning Approaches on Diabetic Retinopathy Prediction

Authors

  • Gowri Prasad  Information Science and Engineering , New Horizon College of Engineering, Bangalore, Karnataka, India
  • Vrinda Raveendran  Information Science and Engineering , New Horizon College of Engineering, Bangalore, Karnataka, India
  • Sri Vidya B M  Information Science and Engineering , New Horizon College of Engineering, Bangalore, Karnataka, India
  • Tejavati Hedge  Information Science and Engineering , New Horizon College of Engineering, Bangalore, Karnataka, India

DOI:

https://doi.org/10.32628/CSEIT206377

Keywords:

Diabetic Retinopathy, Machine Learning, Random Forest Algorithm, Support Vector Algorithm, K-Nearest Algorithm, Neural Networks.

Abstract

Diabetic retinopathy is a eye disorder which is developed due to high blood sugar that affects the neurons in retina. A dangerous fact about this disease is that it can lead to blindness. The possible cure is through detection of disease at early age. This can be done using different machine learning algorithms. This paper does a comparative study on different machine learning algorithms that can be used for early detection of diabetic retinopathy. This study is done to find out the most efficient algorithm suitable for the process and to increase the efficiency of the particular algorithm.

References

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

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Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Gowri Prasad, Vrinda Raveendran, Sri Vidya B M, Tejavati Hedge, " Machine Learning Approaches on Diabetic Retinopathy Prediction" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.341-345, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT206377