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

Publication Type:
Conference Proceeding
Citation:
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010
Issue Date:
2010-12-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010000090.pdf140.82 kB
Adobe PDF
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. © 2010 IEEE.
Please use this identifier to cite or link to this item: