A hypoglycemic episode diagnosis system based on neural networks for type 1 diabetes mellitus

Chan, KY; Ling, SH; Nguyen, HT; Jiang, F

A hypoglycemic episode diagnosis system based on neural networks for type 1 diabetes mellitus

Chan, KY Ling, SH

Nguyen, HT

Jiang, F

Permalink

Publication Type:: Conference Proceeding
Citation:: 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012
Issue Date:: 2012-10-04

Closed Access

	Filename	Description	Size
	2012001723OK.pdf	Published version	367.2 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chan, KY	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Jiang, F	en_US
dc.date.issued	2012-10-04	en_US
dc.identifier.citation	2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012	en_US
dc.identifier.isbn	9781467315098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31509
dc.description.abstract	Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients' blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients' blood glucose levels based on these patients' physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients' data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 IEEE Congress on Evolutionary Computation, CEC 2012	en_US
dc.relation.isbasedon	10.1109/CEC.2012.6256604	en_US
dc.title	A hypoglycemic episode diagnosis system based on neural networks for type 1 diabetes mellitus	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Australia
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients' blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients' blood glucose levels based on these patients' physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients' data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated. © 2012 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/31509