A case-based reasoning framework for prediction of stroke

Chantamit-O-Pas, P; Goyal, M

A case-based reasoning framework for prediction of stroke

Chantamit-O-Pas, P

Goyal, M

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2018, 625 pp. 219 - 227
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Chantamit-O-Pas, P https://orcid.org/0000-0003-1709-8211	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2018, 625 pp. 219 - 227	en_US
dc.identifier.isbn	9789811055072	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132846
dc.description.abstract	© Springer Nature Singapore Pte Ltd. 2018. Case-based reasoning (CBR) has been a popular method in health care sector from the last two decades. It is used for analysis, prediction, diagnosis and recommending treatment for patients. This research purposes a conceptual CBR framework for stroke disease prediction that uses previous case-based knowledge. The outcomes of this approach not only assist in stroke disease decision-making, but also will be very useful for prevention and early treatment of patients.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-981-10-5508-9_21	en_US
dc.title	A case-based reasoning framework for prediction of stroke	en_US
dc.type	Conference Proceeding
utslib.citation.volume	625	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	625	en_US

Abstract:

© Springer Nature Singapore Pte Ltd. 2018. Case-based reasoning (CBR) has been a popular method in health care sector from the last two decades. It is used for analysis, prediction, diagnosis and recommending treatment for patients. This research purposes a conceptual CBR framework for stroke disease prediction that uses previous case-based knowledge. The outcomes of this approach not only assist in stroke disease decision-making, but also will be very useful for prevention and early treatment of patients.

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http://hdl.handle.net/10453/132846