Medical data classification using interval type-2 fuzzy logic system and wavelets

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Medical data classification using interval type-2 fuzzy logic system and wavelets, Applied soft computing, vol. 30, pp. 812-822, doi: 10.1016/j.asoc.2015.02.016.

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Title Medical data classification using interval type-2 fuzzy logic system and wavelets
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh orcid.org/0000-0001-9709-1663
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Applied soft computing
Volume number 30
Start page 812
End page 822
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-05
ISSN 1568-4946
Keyword(s) Breast cancer
Genetic algorithm
Heart disease
Keywords Interval type-2 fuzzy logic system
Medical data classification
Wavelet transformation
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science
Interval type-2 fuzzy logic system
NEURAL-NETWORKS
SETS
DEFUZZIFICATION
ALGORITHM
REDUCTION
DIAGNOSIS
DESIGN
Summary This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.
Language eng
DOI 10.1016/j.asoc.2015.02.016
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075813

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