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Classification of healthcare data using genetic fuzzy logic system and wavelets
journal contribution
posted on 2015-01-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Abbas KhosraviAbbas Khosravi, Douglas CreightonDouglas Creighton, Saeid NahavandiHealthcare 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.
History
Journal
Expert systems with applicationsVolume
42Issue
4Pagination
2184 - 2197Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2014, ElsevierUsage metrics
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No categories selectedKeywords
Breast cancerFuzzy standard additive modelGenetic algorithmHealthcare data classificationHeart diseaseWavelet transformationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringMEDICAL DIAGNOSIS
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