A novel and efficient neuro-fuzzy classifier for medical diagnosis

Chin-Ming Hong, Chih Ming Chen, Syuan-Yi Chen, Chao Yen Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 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 languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages735-741
Number of pages7
Publication statusPublished - 2006 Dec 1
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 2006 Jul 162006 Jul 21

Publication series

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

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • Software

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  • Cite this

    Hong, C-M., Chen, C. M., Chen, S-Y., & Huang, C. Y. (2006). A novel and efficient neuro-fuzzy classifier for medical diagnosis. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 735-741). [1716168] (IEEE International Conference on Neural Networks - Conference Proceedings).