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dc.contributor.authorChan, Kit Yan
dc.contributor.authorLing, S.H.
dc.contributor.authorDillon, Tharam S.
dc.contributor.authorNguyen, H.
dc.contributor.editorGary Fogel
dc.identifier.citationChan, Kit Yan and Ling, Sing Ho and Dillon, Tharam S. and Nguyen, Hung. 2010. Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks, in Fogel, G. (ed), IEEE Congress on Evolutionary Computation (CEC 2010), Jul 18 2010, pp. 1-5. Barcelona, Spain: IEEE.

Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit 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 classification unit 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 including statistical regression, fuzzy regression and genetic programming.

dc.titleClassification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks
dc.typeConference Paper
dcterms.source.titleProceedings of the IEEE congress on evolutionary computation (CEC 2010)
dcterms.source.seriesProceedings of the IEEE congress on evolutionary computation (CEC 2010)
dcterms.source.conferenceIEEE Congress on Evolutionary Computation (CEC 2010)
dcterms.source.conference-start-dateJul 18 2010
dcterms.source.conferencelocationBarcelona, Spain

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curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access

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