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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification

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 induct...

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Main Authors: Tan, Chin Hooi, Tan, Mei Sze, Chang, Siow Wee, Yap, Keem Siah, Yap, Hwa Jen, Wong, Shen Yuong
Format: Article
Published: Taylor's University 2018
Subjects:
Q Science (General)
QH Natural history
TJ Mechanical engineering and machinery
Online Access:http://eprints.um.edu.my/20549/
http://jestec.taylors.edu.my/Special%20Issue%20ICCSIT%202018/ICCSIT18_20.pdf
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spelling my.um.eprints.205492019-03-04T02:09:51Z http://eprints.um.edu.my/20549/ 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 Q Science (General) QH Natural history TJ Mechanical engineering and machinery 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. Taylor's University 2018-07 Article PeerReviewed Tan, Chin Hooi and Tan, Mei Sze and Chang, Siow Wee and Yap, Keem Siah and Yap, Hwa Jen and Wong, Shen Yuong (2018) Genetic algorithm fuzzy logic for medical knowledge-based pattern classification. Journal of Engineering Science and Technology, 13. pp. 242-258. ISSN 1823-4690 http://jestec.taylors.edu.my/Special%20Issue%20ICCSIT%202018/ICCSIT18_20.pdf
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic Q Science (General)
QH Natural history
TJ Mechanical engineering and machinery
spellingShingle Q Science (General)
QH Natural history
TJ Mechanical engineering and machinery
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
description 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.
format Article
author Tan, Chin Hooi
Tan, Mei Sze
Chang, Siow Wee
Yap, Keem Siah
Yap, Hwa Jen
Wong, Shen Yuong
author_facet Tan, Chin Hooi
Tan, Mei Sze
Chang, Siow Wee
Yap, Keem Siah
Yap, Hwa Jen
Wong, Shen Yuong
author_sort Tan, Chin Hooi
title Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
title_short Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
title_full Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
title_fullStr Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
title_full_unstemmed Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
title_sort genetic algorithm fuzzy logic for medical knowledge-based pattern classification
publisher Taylor's University
publishDate 2018
url http://eprints.um.edu.my/20549/
http://jestec.taylors.edu.my/Special%20Issue%20ICCSIT%202018/ICCSIT18_20.pdf
_version_ 1643691312027795456
score 13.211869

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