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

Chan, KY; Ling, SH; Dillon, TS; Nguyen, H

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

Chan, KY Ling, SH

Dillon, TS Nguyen, H

Permalink

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

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (140.82 kB)

View on publisher's site

View statistics

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	Dillon, TS	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010	en_US
dc.identifier.isbn	9781424469109	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16287
dc.description.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. © 2010 IEEE.	en_US
dc.relation.ispartof	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010	en_US
dc.relation.isbasedon	10.1109/CEC.2010.5586320	en_US
dc.title	Classification of hypoglycemic episodes for type 1 diabetes mellitus based on neural networks	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Barcelona, Spain	en_US
dc.location.activity	ISI:000234549400004
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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

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. © 2010 IEEE.

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

http://hdl.handle.net/10453/16287