A Survey on Diagnosis and Analysis of Diabetic Retinopathy using Feature Selection

Authors

  • Amalu Michael  M. Tech Scholar, Department of Computer Science and Engineering, Government Engineering College Idukki, India
  • Deepa S S  Associate Professor, Department of Computer Science and Engineering, Government Engineering College Idukki, India

DOI:

https://doi.org//10.32628/IJSRSET207132

Keywords:

Machine Learning, Diabetic Retinopathy, Feature Selection

Abstract

Diabetic retinopathy is one of the common forms of diabetic eye disease. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal vessels. Machine learning may be a broad multidisciplinary field that has its roots in statistics, algebra, data processing, and information analytics, etc. Machine learning is used to discover patterns from medical data and provide an efficient way to predict diseases.ML is an application of artificial intelligence it collects information from training data. There are several machine learning techniques are used for the diagnosis of diabetic retinopathy. This paper mainly focuses on the survey of such techniques and also various feature selection mechanisms. This study provides the basic categorization of feature selection techniques and discussing their use.

References

  1. K. Shailaja et, al, “Machine Learning in Healthcare: Review” 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
  2. Raid Alzubi et, al “Hypertension Disease Detection via Machine Learning Techniques “IEEE Transactions on Neural Networks and Learning Systems 2018
  3. “Early Diabetes Detection using Machine Learning” International Journal for Innovative Research in Science & Technology 2017
  4. Syed Sifat Rahman et, al “A Machine Learning-Based Approach for Diabetes Detection and Care” 2nd International Journal on Next Generation Computing Technologies (NGCT-2016)
  5. Tao Zheng et, al “A Machine Learning-based Framework to Identify Type 2 Diabetes through Electronic Health Records” International Journal of Medical Informatics 2016
  6. N. Sneha et, al “Analysis of diabetes mellitus for early prediction” Journal of Big data 2019
  7. Yvan Saeys et, al “A review of feature selection techniques in bioinformatics” International Conference on bioinformatics 2016
  8. Peter B. Jensen et, al “Mining electronic health records: towards better research applications and clinical care” 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA 2018)
  9. Karan Bhatia et, al “Diagnosis of Diabetic Retinopathy Using Machine Learning Classification Algorithm” International Conference on Next Generation Computing Technologies (NGCT-2016)
  10. Dhiravidachelvi, E. et, al “Diagnosing Diabetic Retinopathy in Fundus Images” Journal of Computer Science 2018
  11. Swati Gupta et, al “Diagnosis of Diabetic Retinopathy using Machine Learning” Journal of Research and Development 2015
  12. R. Priya et, al “Diagnosis Of Diabetic Retinopathy Using Machine Learning Techniques” ICTACT Journal On Soft Computing, July 2013, Volume: 03, Issue: 04
  13. Yunlei Sun et, al “Diagnosis And Analysis Of Diabetic Retinopathy Based On Electronic Health Records” Special Section On Healthcare Information Technology For The Extreme And Remote Environments 2019

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-02-29

Issue

Volume 7, Issue 1, January-February-2020

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Amalu Michael, Deepa S S, " A Survey on Diagnosis and Analysis of Diabetic Retinopathy using Feature Selection, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 1, pp.170-176, January-February-2020. Available at doi : https://doi.org/10.32628/IJSRSET207132