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On the applications of fuzzy approaches in medical diagnosis and bioinformatics
Journal of the Korean Data & Information Science Society 2018;29:1445-56
Published online November 30, 2018
© 2018 Korean Data and Information Science Society.

Hye-Young Jung1

1Faculty of Liberal Education, Seoul National University
Correspondence to: Associate teaching professor, Faculty of Liberal Education, Seoul National University, Seoul, 08826, Korea. E-mail: hyjunglove@snu.ac.kr
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MIST)(No. 2017R1C1B100506).
Received October 11, 2018; Revised November 18, 2018; Accepted November 19, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The relationships, properties, and objects in the data generated from medical diagnosis and bioinformatics are fundamentally fuzzy. Fuzzy set theory is an ideal framework to deal with such data. Fuzzy set theory is considered to be an extended set theory to deal with uncertainty of boundary and classification. In this paper, we illustrate how fuzzy approaches based on fuzzy set theory can be applied to data in medical diagnostics and bioinformatics with various examples.
Keywords : Bioinformatics, fuzzy set theory, medical diagnosis, uncertainty.