Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/100673
DC FieldValueLanguage
dc.contributor.authorSantos-Pereira, Judith-
dc.contributor.authorGruenwald, Le-
dc.contributor.authorBernardino, Jorge-
dc.date.accessioned2022-07-08T08:27:13Z-
dc.date.available2022-07-08T08:27:13Z-
dc.date.issued2021-
dc.identifier.issn13191578pt
dc.identifier.urihttps://hdl.handle.net/10316/100673-
dc.description.abstractThe healthcare industry has become increasingly challenging, requiring retrieval of knowledge from large amounts of complex data to find the best treatments. Several works have suggested the use of Data Mining tools to overcome the challenges; however, none of them has suggested the best tool to do so. To fill this gap, this paper presents a survey of popular open-source data mining tools in which data mining tool selection criteria based on healthcare application requirements is proposed and the best ones using the proposed selection criteria are identified. The following popular open-source data mining tools are assessed: KNIME, R, RapidMiner, Scikit-learn, and Spark. The study shows that KNIME and RapidMiner provide the largest coverage of healthcare data mining requirementspt
dc.language.isoengpt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt
dc.subjectData miningpt
dc.subjectHealthcarept
dc.subjectOpen-source data mining toolspt
dc.titleTop data mining tools for the healthcare industrypt
dc.typearticle-
degois.publication.titleJournal of King Saud University - Computer and Information Sciencespt
dc.peerreviewedyespt
dc.identifier.doi10.1016/j.jksuci.2021.06.002pt
dc.date.embargo2021-01-01*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.openairetypearticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.orcid0000-0001-9660-2011-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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