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Medical diagnosis by fuzzy standard additive model with wavelets

Nguyen,T, Khosravi,A, Creighton,D and Nahavandi,S 2014, Medical diagnosis by fuzzy standard additive model with wavelets, in FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1937-1944, doi: 10.1109/FUZZ-IEEE.2014.6891861.

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Title Medical diagnosis by fuzzy standard additive model with wavelets
Author(s) Nguyen,TORCID iD for Nguyen,T orcid.org/0000-0001-9709-1663
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Creighton,DORCID iD for Creighton,D orcid.org/0000-0002-9217-1231
Nahavandi,SORCID iD for Nahavandi,S orcid.org/0000-0002-0360-5270
Conference name IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE International Conference on Fuzzy Systems
Start page 1937
End page 1944
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) breast cancer
fuzzy system
heart disease
medical diagnosis
wavelet transformation
Summary This paper proposes a combination of fuzzy standard additive model (SAM) with wavelet features for medical diagnosis. Wavelet transformation is used to reduce the dimension of high-dimensional datasets. This helps to improve the convergence speed of supervised learning process of the fuzzy SAM, which has a heavy computational burden in high-dimensional data. Fuzzy SAM becomes highly capable when deployed with wavelet features. This combination remarkably reduces its computational training burden. The performance of the proposed methodology is examined for two frequently used medical datasets: the lump breast cancer and heart disease. Experiments are deployed with a five-fold cross validation. Results demonstrate the superiority of the proposed method compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. Faster convergence but higher accuracy shows a win-win solution of the proposed approach.
ISBN 9781479920723
ISSN 1098-7584
Language eng
DOI 10.1109/FUZZ-IEEE.2014.6891861
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069612

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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