Leveraging Feature Selection Algorithms for Early Detection of Type-2 Diabetes

Authors

  • Lincy M Government College of Technology,Coimbatore
  • A. Meena Kowshalya

Abstract

Numerous feature selection algorithms have been proposed in the past to solve the curse of dimensionality problem. The choice of apt feature selection algorithm is still a fundamental area of research. Feature selection is a process of identifying and removing irrelevant features and retaining only the highest contributing feature set. Feature selection algorithms are primarily used in applications like healthcare where the classification accuracy needs to be high. This study proposes three simple algorithms namely Mean based Feature Selection (MbFS), Correlation based Feature Selection (CbFS) and Recursive Elimination based Feature Selection (REbFS) for feature selection that efficiently predict early type-2 diabetes. The proposed algorithms are compared with Greedy Stepwise Search Feature Selection algorithm. Exploiting multilayer perceptron classifier, the mean based feature selection yields an accuracy of 80.1 and 79.2%, the correlation based feature selection yields an accuracy of 80.5 and 80.5%, and the recursive elimination based feature selection yields an accuracy of 64.9 and 75% for 768 instances and 2000 instances respectively.

Keywords: Feature selection, mean based, correlation, recursive elimination, type 2 diabetes

Cite this Article M. Lincy, A. Meena Kowshalya. Leveraging Feature Selection Algorithms for Early Detection of Type-2 Diabetes. Journal of Computer Technology & Applications. 2020; 11(1): 13–20p.

Downloads

Published

2020-05-20

Issue

Vol. 11 No. 1 (2020)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.