Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network

De Campos Souza, PV; Wang, YK; Lughofer, E

Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network

De Campos Souza, PV Wang, YK Lughofer, E

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2020, 2020-July
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	De Campos Souza, PV
dc.contributor.author	Wang, YK
dc.contributor.author	Lughofer, E
dc.date	2020-07-19
dc.date.accessioned	2024-03-31T15:40:29Z
dc.date.available	2024-03-31T15:40:29Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2020, 2020-July
dc.identifier.isbn	9781728169323
dc.identifier.issn	1098-7584
dc.identifier.uri	http://hdl.handle.net/10453/177378
dc.description.abstract	Cancer treatment is extremely aggressive and, in addition to causing considerable discomfort, can lead to death. Therefore, identifying aspects related to treatment assertiveness may be efficient for reducing the mortality rate of cancer patients. This paper seeks to identify the prognosis of cancer treatment survival through hybrid techniques based on the autonomous fuzzification process and artificial neural networks. The public dataset on cancer mortality is the source for conducting treatment assertiveness rating tests. The hybrid model had its results compared to other models present in the pattern classification literature with superior accuracy and identification of people likely to survive treatment (90.46%), and the fuzzy rules obtained with the execution of the model corroborate the high assertiveness of the model, even surpassing state of the art for the theme.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.ispartofseries	IEEE International Conference on Fuzzy Systems
dc.relation.isbasedon	10.1109/FUZZ48607.2020.9177561
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network
dc.type	Conference Proceeding
utslib.citation.volume	2020-July
utslib.location.activity	Glasgow, UK
utslib.for	0801 Artificial Intelligence and Image Processing
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2024-03-31T15:40:27Z
pubs.finish-date	2020-07-24
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	2020-July
dc.location	Piscataway, USA

Abstract:

Cancer treatment is extremely aggressive and, in addition to causing considerable discomfort, can lead to death. Therefore, identifying aspects related to treatment assertiveness may be efficient for reducing the mortality rate of cancer patients. This paper seeks to identify the prognosis of cancer treatment survival through hybrid techniques based on the autonomous fuzzification process and artificial neural networks. The public dataset on cancer mortality is the source for conducting treatment assertiveness rating tests. The hybrid model had its results compared to other models present in the pattern classification literature with superior accuracy and identification of people likely to survive treatment (90.46%), and the fuzzy rules obtained with the execution of the model corroborate the high assertiveness of the model, even surpassing state of the art for the theme.

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http://hdl.handle.net/10453/177378