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.

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Title Classification of healthcare data using genetic fuzzy logic system and wavelets
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh
Khosravi, AbbasORCID iD for Khosravi, Abbas
Creighton, DouglasORCID iD for Creighton, Douglas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
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
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