Genetic algorithm fuzzy logic for medical knowledge-based pattern classification

Chin Hooi Tan, Mei Sze Tan, Siow Wee Chang, Keem Siah Yap, Hwa Jen Yap, Shen Yuong Wong

Research output: Contribution to journalArticle

Abstract

Hybrid of genetic algorithm and fuzzy logic in genetic fuzzy system exemplifies the advantage of best heuristic search with ease of understanding and interpretability. This research proposed an algorithm named Genetic Algorithm Fuzzy Logic (GAFL) with Pittsburg approach for rules learning and induction in genetic fuzzy system knowledge discovery. The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. GAFL, with simplistic binary coding scheme using Pittsburg approach managed to exploit the potential of genetic fuzzy inference system with ease of comprehension in fuzzy rules induction in knowledge pattern recognition. The proposed algorithm was tested with three public available medical datasets, which are Wisconsin Breast Cancer (WBC) dataset, Pima Indian Diabetes dataset (PID), Parkinson Disease dataset (PD) and one locally collected oral cancer dataset. The results obtained showed that GAFL outperformed most of the other models that acknowledged from the previous studies. GAFL possessed the advantage of fuzzy rules extraction feature apart from conventional classification technique compared to other models which are lack of fuzzy interpretation. It is easier to interpret and understand fuzzy value in contrast to continuous or range value. GAFL outperformed the other algorithms in terms of accuracy without compromising on interpretability. It is vital to obtain high accuracy in medical pattern recognition especially when dealing with critical illness.

Original languageEnglish
Pages (from-to)242-258
Number of pages17
JournalJournal of Engineering Science and Technology
Volume13
Issue numberSpecial Issue on ICCSIT 2018
Publication statusPublished - 01 Jul 2018

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Fuzzy logic
Pattern recognition
Genetic algorithms
Fuzzy rules
Fuzzy systems
Fuzzy inference
Medical problems
Data mining
Feature extraction

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Tan, C. H., Tan, M. S., Chang, S. W., Yap, K. S., Yap, H. J., & Wong, S. Y. (2018). Genetic algorithm fuzzy logic for medical knowledge-based pattern classification. Journal of Engineering Science and Technology, 13(Special Issue on ICCSIT 2018), 242-258.
Tan, Chin Hooi ; Tan, Mei Sze ; Chang, Siow Wee ; Yap, Keem Siah ; Yap, Hwa Jen ; Wong, Shen Yuong. / Genetic algorithm fuzzy logic for medical knowledge-based pattern classification. In: Journal of Engineering Science and Technology. 2018 ; Vol. 13, No. Special Issue on ICCSIT 2018. pp. 242-258.
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Tan, CH, Tan, MS, Chang, SW, Yap, KS, Yap, HJ & Wong, SY 2018, 'Genetic algorithm fuzzy logic for medical knowledge-based pattern classification', Journal of Engineering Science and Technology, vol. 13, no. Special Issue on ICCSIT 2018, pp. 242-258.

Genetic algorithm fuzzy logic for medical knowledge-based pattern classification. / Tan, Chin Hooi; Tan, Mei Sze; Chang, Siow Wee; Yap, Keem Siah; Yap, Hwa Jen; Wong, Shen Yuong.

In: Journal of Engineering Science and Technology, Vol. 13, No. Special Issue on ICCSIT 2018, 01.07.2018, p. 242-258.

Research output: Contribution to journalArticle

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Tan CH, Tan MS, Chang SW, Yap KS, Yap HJ, Wong SY. Genetic algorithm fuzzy logic for medical knowledge-based pattern classification. Journal of Engineering Science and Technology. 2018 Jul 1;13(Special Issue on ICCSIT 2018):242-258.