A novel and efficient neuro-fuzzy classifier for medical diagnosis

Chin Ming Hong; Chih Ming Chen; Shyuan Yi Chen; Chao Yen Huang

A novel and efficient neuro-fuzzy classifier for medical diagnosis

Chin Ming Hong^*, Chih Ming Chen, Shyuan Yi Chen, Chao Yen Huang

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.

Original language	English
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Pages	735-741
Number of pages	7
Publication status	Published - 2006
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: 2006 Jul 16 → 2006 Jul 21

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Other

Other	International Joint Conference on Neural Networks 2006, IJCNN '06
Country/Territory	Canada
City	Vancouver, BC
Period	2006/07/16 → 2006/07/21

ASJC Scopus subject areas

Software

Cite this

A novel and efficient neuro-fuzzy classifier for medical diagnosis. / Hong, Chin Ming; Chen, Chih Ming; Chen, Shyuan Yi et al.
International Joint Conference on Neural Networks 2006, IJCNN '06. 2006. p. 735-741 1716168 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hong, CM, Chen, CM, Chen, SY & Huang, CY 2006, A novel and efficient neuro-fuzzy classifier for medical diagnosis. in International Joint Conference on Neural Networks 2006, IJCNN '06., 1716168, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 735-741, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canada, 2006/07/16.

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title = "A novel and efficient neuro-fuzzy classifier for medical diagnosis",

abstract = "This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.",

author = "Hong, {Chin Ming} and Chen, {Chih Ming} and Chen, {Shyuan Yi} and Huang, {Chao Yen}",

year = "2006",

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AU - Chen, Chih Ming

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AU - Huang, Chao Yen

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AB - This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.

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M3 - Conference contribution

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BT - International Joint Conference on Neural Networks 2006, IJCNN '06

T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

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A novel and efficient neuro-fuzzy classifier for medical diagnosis

Abstract

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ASJC Scopus subject areas

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