Classification of healthcare data using genetic fuzzy logic system and wavelets Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Classification of healthcare data using genetic fuzzy logic system and wavelets, Expert systems with applications, vol. 42, no. 4, pp. 2184-2197, doi: 10.1016/j.eswa.2014.10.027. Attached Files Name Description MIMEType Size Downloads Title Classification of healthcare data using genetic fuzzy logic system and wavelets Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Khosravi, Abbas orcid.org/0000-0001-6927-0744 Creighton, Douglas orcid.org/0000-0002-9217-1231 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Expert systems with applications Volume number 42 Issue number 4 Start page 2184 End page 2197 Total pages 14 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2015-03 ISSN 0957-4174 Keyword(s) Breast cancer Fuzzy standard additive model Genetic algorithm Healthcare data classification Heart disease Wavelet transformation Summary Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM 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 helpful as a decision support system for medical practitioners in the healthcare practice. Language eng DOI 10.1016/j.eswa.2014.10.027 Field of Research 080102 Artificial Life Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2014, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30068119 Document type: Journal Article Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 09 Dec 2014, 10:31:34 EST Tue, 09 Dec 2014, 10:34:45 EST Tue, 09 Dec 2014, 13:31:30 EST Tue, 02 Jun 2015, 14:41:07 EST Tue, 11 Aug 2015, 06:07:14 EST Mon, 17 Aug 2015, 09:27:31 EST Mon, 17 Aug 2015, 09:29:44 EST Mon, 12 Oct 2015, 15:44:33 EST Fri, 16 Dec 2016, 09:22:43 EST Mon, 23 Jan 2017, 15:17:02 EST Mon, 23 Jan 2017, 15:19:06 EST Mon, 28 May 2018, 12:12:43 EST Filtered Full Citation counts:   Cited 32 times in TR Web of Science Cited 38 times in Scopus Search Google Scholar Access Statistics: 346 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Tue, 09 Dec 2014, 10:34:45 EST

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