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

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.

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)

DOI

10.1109/CEC.2010.5586320

ISBN

9781424469093

School

Digital Ecosystems and Business Intelligence Institute (DEBII)

Remarks

Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

URI

http://hdl.handle.net/20.500.11937/34068

Collection

Curtin Research Publications