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

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dc.contributor.author Chan, K
dc.contributor.author Ling, SS
dc.contributor.author Dillon, TS
dc.contributor.author Nguyen, HT
dc.date.accessioned 2012-02-02T03:57:09Z
dc.date.issued 2011-01
dc.identifier.citation Expert Systems With Applications, 2011, 38 (8), pp. 9799 - 9808
dc.identifier.issn 0957-4174
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.
dc.publisher Pergamon-Elsevier Science Ltd
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1016/j.eswa.2011.02.020
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.subject Marquardt Algorithm, Feature-Selection, Yager-Inference, Classification, Disease, Artificial Intelligence & Image Processing
dc.subject Marquardt Algorithm; Feature-Selection; Yager-Inference; Classification; Disease; Artificial Intelligence & Image Processing
dc.title Diagnosis Of Hypoglycemic Episodes Using A Neural Network Based Rule Discovery System
dc.type Journal Article
dc.parent Expert Systems With Applications
dc.journal.volume 8
dc.journal.volume 38
dc.journal.number 8 en_US
dc.publocation Oxford, UK en_US
dc.publocation New York
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 0102 Applied Mathematics
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.edition 1st
dc.custom en_US
dc.date.activity en_US
dc.location.activity WOS:000290237500085 en_US
dc.location.activity WOS:000290237500085
dc.description.keywords Marquardt Algorithm; Feature-Selection; Yager-Inference; Classification; Disease en_US
dc.description.keywords exercise, exercise fatigue, fatigue, muscle fatigue, exercise intensity, exercise duration
dc.description.keywords Marquardt Algorithm
dc.description.keywords Feature-Selection
dc.description.keywords Yager-Inference
dc.description.keywords Classification
dc.description.keywords Disease
dc.staffid 840115 en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies


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