An application of belief merging for the diagnosis of oral cancer

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dc.contributor.author Kareem, Sameem Abdul
dc.contributor.author Pozos-Parra, Pilar
dc.contributor.author Wilson, Nic
dc.date.accessioned 2018-02-12T16:07:31Z
dc.date.available 2018-02-12T16:07:31Z
dc.date.issued 2017-04-18
dc.identifier.citation Kareem, S. A., Pozos-Parra, P. and Wilson, N. (2017) 'An application of belief merging for the diagnosis of oral cancer', Applied Soft Computing, 61, pp. 1105-1112. doi: 10.1016/j.asoc.2017.01.055 en
dc.identifier.volume 61 en
dc.identifier.startpage 1105 en
dc.identifier.endpage 1112 en
dc.identifier.issn 1568-4946
dc.identifier.uri http://hdl.handle.net/10468/5445
dc.identifier.doi 10.1016/j.asoc.2017.01.055
dc.description.abstract Machine learning employs a variety of statistical, probabilistic, fuzzy and optimization techniques that allow computers to “learn” from examples and to detect hard-to-discern patterns from large, noisy or complex datasets. This capability is well-suited to medical applications, and machine learning techniques have been frequently used in cancer diagnosis and prognosis. In general, machine learning techniques usually work in two phases: training and testing. Some parameters, with regards to the underlying machine learning technique, must be tuned in the training phase in order to best “learn” from the dataset. On the other hand, belief merging operators integrate inconsistent information, which may come from different sources, into a unique consistent belief set (base). Implementations of merging operators do not require tuning any parameters apart from the number of sources and the number of topics to be merged. This research introduces a new manner to “learn” from past examples using a non parametrised technique: belief merging. The proposed method has been used for oral cancer diagnosis using a real-world medical dataset. The results allow us to affirm the possibility of training (merging) a dataset without having to tune the parameters. The best results give an accuracy of greater than 75%. en
dc.description.sponsorship Universiti Malaya, University of Malaya (Sabbatocal Leave); Consejo Nacional de Ciencia y Tecnología, and Universidad Juárez Autónoma de Tabasco (CONACyT and UJAT, Programme Postdoctoral and Sabbatical Stays Abroad for the Consolidation of Research Groups) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S156849461730087X
dc.rights © 2017 Published by Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Artificial intelligence en
dc.subject Knowledge modelling en
dc.subject Decision support systems en
dc.subject Belief merging en
dc.subject Computer science en
dc.subject Oral cancer en
dc.title An application of belief merging for the diagnosis of oral cancer en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: n.wilson@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 24 months after publication by request of the publisher. en
dc.check.date 2019-03-18
dc.date.updated 2018-02-12T15:51:54Z
dc.description.version Accepted Version en
dc.internal.rssid 425476467
dc.contributor.funder Universiti Malaya en
dc.contributor.funder Consejo Nacional de Ciencia y Tecnología en
dc.contributor.funder Universidad Juárez Autónoma de Tabasco en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Applied Soft Computing en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress n.wilson@ucc.ie en


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© 2017 Published by Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2017 Published by Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
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