Early Detection of Type-2 Diabetes Using Federated Learning

Authors

  • M. Lincy  Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India
  • Dr. A. Meena Kowshalya  Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.32628/IJSRSET207644

Keywords:

Federated learning, decentralized model, Differential Privacy, Feature Selection, Type 2 diabetes

Abstract

Data privacy and security are incredibly important in the healthcare industry. Federated learning is a new way of training a machine learning algorithm using distributed data which is not hosted in a centralized server. Numerous centralized machine learning models exists in literature but none offers privacy to users’ data. This paper proposes a federated learning approach for early detection of Type-2 Diabetes among patients. A simple federated architecture is exploited for early detection of Type-2 diabetes. We compare the proposed federated learning model against our centralised approach. Experimental results prove that the federated learning model ensures significant privacy over centralised learning model whereas compromising accuracy for a subtle extend.

References

  1. Sajratul Yakin Rubaiat, Md Monibor Rahman, Md.Kamrul Hasan, 2018, “Important Feature Selection & Accuracy Comparisons of Different Machine Learning Models for Early Diabetes Detection”, International Conference on Innovation in Engineering and Technology (ICIET).
  2. H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, 2018, “Type 2 diabetes model based on data mining”, Informatics in Medicine Unlocked, vol. 10, pp. 100–107.
  3. Aliza Ahmad, Aida Mustapha, Eliza Dianna Zahadi, Norhayati Masah, Nur Yasmin Yahaya, 2011, ” Comparison between Neural Networks against Decision Tree in Improving Prediction Accuracy for Diabetes Mellitus”, Digital Information Processing and Communications, Springer.
  4. Dilip Kumar Choubey, Sanchita Paul & Santosh Kumar, Shankar Kumar, 2017 , “Classification of Pima indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection”, Communication and Computing Systems – Prasad et al. (Eds) Taylor & Francis Group, London, ISBN 978-1-138-02952-1.
  5. Kamer Kayaer, Tulay Yildirim, 2003, “Medical diagnosis on pima indian diabetes using general regression neural networks”, Proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP), pp. 181–184.
  6. Manjeevan Seera, Chee Peng Lim, 2014, “A hybrid intelligent system for medical data classification”, Expert Systems with Applications, vol. 41, no. 5, pp. 2239–2249.
  7. YoichiHayashi, ShonosukeYukita, 2016, “Rule extraction using recursive-rule extraction algorithm with j48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the pima indian dataset,” Informatics in Medicine Unlocked, vol. 2, pp. 92–104.
  8. Humar Kahramanli, Novruz Allahverdi, 2008, “Design of a hybrid system for the diabetes and heart diseases”, Expert systems with applications, vol. 35, no. 1-2, pp. 82–89.
  9. R. Priyadarshini, N. Dash, and R. Mishra, 2014, “A novel approach to predict diabetes mellitus using modified extreme learning machine”, Electronics and Communication Systems (ICECS), International Conference on. IEEE, pp. 1–5.
  10. M.Lincy, Dr.A.MeenaKowshalya, “Leveraging Feature Selection Algorithms for Early Detection of Type-2 Diabetes”, Journal of Computer Technology & Applications, May 2020, ISSN: 2229-6964, Volume 11.
  11. Evita Bakopoulou, Bálint Tillman, and Athina Markopoulou, “A Federated Learning Approach for Mobile Packet Classification”, arXiv: 1907.13113v1, 2019.
  12. Li Huang, Dianbo Liu, “Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records”, Journal of Biomedical Informatics, Volume 99, November 2019.
  13. Xin Yao, Chaofeng Huang, Lifeng Sun, “Two-Stream Federated Learning: Reduce the Communication Costs”, IEEE Visual Communications and Image Processing (VCIP), April 2019.
  14. Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang,, Hao Deng, and Dianbo Liu, “LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical data”, arXiv:1811.12629v3, Aug 2019.
  15. Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao, “Federated Learning in Mobile Edge Networks: A Comprehensive Survey”, Computer Science, Engineering, ArXiv 2019.
  16. Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra, “Federated Learning with Non-IID Data”, Machine Learning, arXiv:1806.00582, June 2018.
  17. Theodora S. Brisimia, Ruidi Chena, Theofanie Melac, Alex Olshevskya, Ioannis Ch. Paschalidisa, Wei Shia,d, “Federated learning of predictive models from federated Electronic Health Records”, International Journal of Medical Informatics, Volume 112, April 2018.
  18. Nguyen H. Tran, Wei Bao, Albert Zomaya, Minh N.H. Nguyen, Choong Seon Hong, “Federated Learning over Wireless Networks: Optimization Model Design and Analysis”, IEEE Conference on Computer Communications, June 2019.
  19. Dianbo Liu, Dmitriy Dligach, Timothy Miller,”Two-Stage Federated Phenotyping and Patient Representation Learning”, arXiv:1908.05596 , August 2019.
  20. Dataset 1(768 instances) https://www.kaggle.com/uciml/pima-indians-diabetes-database/download
  21. Dataset 2(2000 instances) https://www.kaggle.com/johndasilva/diabetes/download
  22. A. Meena Kowshalya, R. Madhumathi, N. Gopika, 2019, “Correlation Based Feature Selection Algorithms for Varying Datasets of Different Dimensionality”, Wireless Personal Communications, October, Volume 108, Issue 3, pp 1977-1993.
  23. Gopika, N, A. Meena Kowshalya "Correlation based feature selection algorithm for machine learning." In 2018 3rd International Conference on Communication and Electronics Systems (ICCES), pp. 692-695. IEEE, 2018.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-12-30

Issue

Volume 7, Issue 6, November-December-2020

Section

Research Articles

License

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

How to Cite

[1]
M. Lincy, Dr. A. Meena Kowshalya "Early Detection of Type-2 Diabetes Using Federated Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 6, pp.257-267, November-December-2020. Available at doi : https://doi.org/10.32628/IJSRSET207644