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Cluster analysis for breast cancer patterns identification

2021Conference paper Restricted access
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dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorAlves, Filipe
dc.contributor.authorRocha, Ana Maria A.C.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2022-04-05T14:22:11Z
dc.date.available2022-04-05T14:22:11Z
dc.date.issued2021
dc.description.abstractSafety in patient decision-making is one of the major health care challenges. Computational support in establishing diagnoses and preventing errors will contribute to an enhancement in doctor-patient communication. This work performs a three-dimensional cluster analysis, using k-means algorithm, to identify patterns in a breast cancer database. The methodology proposed can be useful to identify patterns in the database that are normally difficult to be noted by classical methods, such as statistical methods. The three-dimensional cluster approach was explored combining three variables at once. The k-means algorithm is used to recognize the hidden patterns on the database. Sub-clusters are used to separate the benign and malignant tumors inside the global cluster. The results present effective analyses of three different clusters based on different combinations between variables. Thus, health professionals can obtain a better understanding of the properties of different types of tumor, identifying the mined abstract tumor features, through the cluster data analysis.pt_PT
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020. Filipe Alves is supported by FCT Grant Reference SFRH/BD/143745/2019.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAzevedo, Beatriz Flamia; Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I. (2021). Cluster analysis for breast cancer patterns identification. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 508-514. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_37pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25358
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationUIDB/05757/2020.pt_PT
dc.relationALGORITMI Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCluster analysispt_PT
dc.subjectDisease diagnosispt_PT
dc.subjectBreast cancerpt_PT
dc.titleCluster analysis for breast cancer patterns identificationpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleALGORITMI Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT
oaire.citation.conferencePlaceBragançapt_PT
oaire.citation.endPage514pt_PT
oaire.citation.startPage507pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAzevedo
person.familyNameAlves
person.familyNamePereira
person.givenNameBeatriz Flamia
person.givenNameFilipe
person.givenNameAna I.
person.identifier.ciencia-id181E-855C-E62C
person.identifier.ciencia-idDF1B-F14B-A8BC
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0002-8527-7409
person.identifier.orcid0000-0002-8387-391X
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridV-5791-2017
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id57195267974
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication200d05f8-7834-47d4-872d-1b6b82a323d2
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