An application of belief merging for the diagnosis of oral cancer

dc.contributor.authorKareem, Sameem Abdul
dc.contributor.authorPozos-Parra, Pilar
dc.contributor.authorWilson, Nic
dc.contributor.funderUniversiti Malayaen
dc.contributor.funderConsejo Nacional de Ciencia y Tecnologíaen
dc.contributor.funderUniversidad Juárez Autónoma de Tabascoen
dc.date.accessioned2018-02-12T16:07:31Z
dc.date.available2018-02-12T16:07:31Z
dc.date.issued2017-04-18
dc.date.updated2018-02-12T15:51:54Z
dc.description.abstractMachine 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.sponsorshipUniversiti 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.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKareem, 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.2017.01.055en
dc.identifier.doi10.1016/j.asoc.2017.01.055
dc.identifier.endpage1112en
dc.identifier.issn1568-4946
dc.identifier.journaltitleApplied Soft Computingen
dc.identifier.startpage1105en
dc.identifier.urihttps://hdl.handle.net/10468/5445
dc.identifier.volume61en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttp://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.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectArtificial intelligenceen
dc.subjectKnowledge modellingen
dc.subjectDecision support systemsen
dc.subjectBelief mergingen
dc.subjectComputer scienceen
dc.subjectOral canceren
dc.titleAn application of belief merging for the diagnosis of oral canceren
dc.typeArticle (peer-reviewed)en
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