Diagnosis Of Hypoglycemic Episodes Using A Neural Network Based Rule Discovery System

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dc.contributor.author Chan, K Y en_US
dc.contributor.author Ling, Steve en_US
dc.contributor.author Dillon, Tharam en_US
dc.contributor.author Nguyen, Hung en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T03:57:09Z
dc.date.available 2012-02-02T03:57:09Z
dc.date.issued 2011 en_US
dc.identifier 2010004641 en_US
dc.identifier.citation Chan Kelly et al. 2011, 'Diagnosis Of Hypoglycemic Episodes Using A Neural Network Based Rule Discovery System', Pergamon-Elsevier Science Ltd, vol. 38, no. 8, pp. 9799-9808. en_US
dc.identifier.issn 0957-4174 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/14469
dc.description.abstract Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients' physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients. en_US
dc.language en_US
dc.publisher Pergamon-Elsevier Science Ltd en_US
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.eswa.2011.02.020 en_US
dc.rights NOTICE: This is the author’s version of a work that was accepted for publication by Elsevier. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published by Elsevier. en_US
dc.title Diagnosis Of Hypoglycemic Episodes Using A Neural Network Based Rule Discovery System en_US
dc.parent Expert Systems With Applications en_US
dc.journal.volume 38 en_US
dc.journal.number 8 en_US
dc.publocation Oxford, UK en_US
dc.identifier.startpage 9799 en_US
dc.identifier.endpage 9808 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 010200 en_US
dc.personcode 0000073865 en_US
dc.personcode 106694 en_US
dc.personcode 030567 en_US
dc.personcode 840115 en_US
dc.percentage 100 en_US
dc.classification.name Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity WOS:000290237500085 en_US
dc.description.keywords Marquardt Algorithm; Feature-Selection; Yager-Inference; Classification; Disease en_US
dc.staffid 840115 en_US


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