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dc.contributor.authorOrtiz Barrios, Miguel Angelspa
dc.contributor.authorNugent, Chrisspa
dc.contributor.authorCleland, Ianspa
dc.contributor.authorDonnelly, Markspa
dc.contributor.authorVerikas, Antanasspa
dc.date.accessioned2020-04-17T21:26:14Z
dc.date.available2020-04-17T21:26:14Z
dc.date.issued2019-07-03
dc.identifier.urihttps://hdl.handle.net/11323/6219spa
dc.description.abstractThe number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research.spa
dc.language.isoeng
dc.publisherUniversidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectAssistive technologyspa
dc.subjectClassifierspa
dc.subjectDementiaspa
dc.subjectFAHPspa
dc.subjectTOPSISspa
dc.titleSelecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: a multicriteria frameworkspa
dc.typePre-Publicaciónspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.type.coarhttp://purl.org/coar/resource_type/c_816bspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/preprintspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTOTRspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_14cbspa


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