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Medical diagnosis by fuzzy standard additive model with wavelets
conference contribution
posted on 2023-10-26, 04:16 authored by Thanh Thi NguyenThanh Thi Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, Saeid NahavandiThis 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.
History
Pagination
1937 - 1944Location
Beijing, ChinaPublisher DOI
Start date
2014-07-06End date
2014-07-11ISSN
1098-7584ISBN-13
9780000000000.0Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy SystemsUsage metrics
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