Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system

San, PP; Ling, SH; Nuryani; Nguyen, H

Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system

San, PP Ling, SH

Nuryani Nguyen, H

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2014, 44 (8), pp. 1338 - 1349
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	San, PP	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nuryani	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2014, 44 (8), pp. 1338 - 1349	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37512
dc.identifier.uri	http://hdl.handle.net/10453/33127
dc.identifier.uri	http://hdl.handle.net/10453/37511
dc.identifier.uri	http://hdl.handle.net/10453/37513
dc.description.abstract	This paper focuses on the hybridization technology using rough sets concepts and neural computing for decision and classification purposes. Based on the rough set properties, the lower region and boundary region are defined to partition the input signal to a consistent (predictable) part and an inconsistent (random) part. In this way, the neural network is designed to deal only with the boundary region, which mainly consists of an inconsistent part of applied input signal causing inaccurate modeling of the data set. Owing to different characteristics of neural network (NN) applications, the same structure of conventional NN might not give the optimal solution. Based on the knowledge of application in this paper, a block-based neural network (BBNN) is selected as a suitable classifier due to its ability to evolve internal structures and adaptability in dynamic environments. This architecture will systematically incorporate the characteristics of application to the structure of hybrid rough-block-based neural network (R-BBNN). A global training algorithm, hybrid particle swarm optimization with wavelet mutation is introduced for parameter optimization of proposed R-BBNN. The performance of the proposed R-BBNN algorithm was evaluated by an application to the field of medical diagnosis using real hypoglycemia episodes in patients with Type 1 diabetes mellitus. The performance of the proposed hybrid system has been compared with some of the existing neural networks. The comparison results indicated that the proposed method has improved classification performance and results in early convergence of the network. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2013.2283296	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diabetes Mellitus, Type 1	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Blood Glucose	en_US
dc.subject.mesh	Electrocardiography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Electric Impedance	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	44	en_US
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
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/Strength - CHT - Health Technologies
utslib.copyright.status	in_progress
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	44	en_US

Abstract:

This paper focuses on the hybridization technology using rough sets concepts and neural computing for decision and classification purposes. Based on the rough set properties, the lower region and boundary region are defined to partition the input signal to a consistent (predictable) part and an inconsistent (random) part. In this way, the neural network is designed to deal only with the boundary region, which mainly consists of an inconsistent part of applied input signal causing inaccurate modeling of the data set. Owing to different characteristics of neural network (NN) applications, the same structure of conventional NN might not give the optimal solution. Based on the knowledge of application in this paper, a block-based neural network (BBNN) is selected as a suitable classifier due to its ability to evolve internal structures and adaptability in dynamic environments. This architecture will systematically incorporate the characteristics of application to the structure of hybrid rough-block-based neural network (R-BBNN). A global training algorithm, hybrid particle swarm optimization with wavelet mutation is introduced for parameter optimization of proposed R-BBNN. The performance of the proposed R-BBNN algorithm was evaluated by an application to the field of medical diagnosis using real hypoglycemia episodes in patients with Type 1 diabetes mellitus. The performance of the proposed hybrid system has been compared with some of the existing neural networks. The comparison results indicated that the proposed method has improved classification performance and results in early convergence of the network. © 2013 IEEE.

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http://hdl.handle.net/10453/33127