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Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks

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Open access
Authors
Chan, Kit Yan
Ling, S.H.
Dillon, Tharam S.
Nguyen, H.
Date
2010
Type
Conference Paper

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Citation
Chan, 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.
Source Title
Proceedings of the IEEE congress on evolutionary computation (CEC 2010)
Source Conference
IEEE Congress on Evolutionary Computation (CEC 2010)
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School
Digital Ecosystems and Business Intelligence Institute (DEBII)
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Abstract

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.