Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9932
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLee, WC-
dc.creatorHung, FH-
dc.creatorTsang, KF-
dc.creatorTung, HC-
dc.creatorLau, WH-
dc.creatorRakocevic, V-
dc.creatorLai, LL-
dc.date.accessioned2015-10-13T08:28:20Z-
dc.date.available2015-10-13T08:28:20Z-
dc.identifier.urihttp://hdl.handle.net/10397/9932-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Lee, W.C.; Hung, F.H.; Tsang, K.F.; Tung, H.C.; Lau, W.H.; Rakocevic, V.; Lai, L.L. A Speedy Cardiovascular Diseases Classifier Using Multiple Criteria Decision Analysis. Sensors 2015, 15, 1312-1320 is available at https://dx.doi.org/10.3390/s150101312en_US
dc.subjectAnalytic hierarchy processen_US
dc.subjectCardiovascular diseases classifieren_US
dc.subjectElectrocardiogramen_US
dc.subjectMultiple criteria decision analysisen_US
dc.subjectSupport vector machineen_US
dc.titleA speedy cardiovascular diseases classifier using multiple criteria decision analysisen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1312en_US
dc.identifier.epage1320en_US
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.doi10.3390/s150101312en_US
dcterms.abstractEach year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, Jan. 2015, v. 15, no. 1, p. 1312-1320-
dcterms.isPartOfSensors-
dcterms.issued2015-
dc.identifier.scopus2-s2.0-84937193736-
dc.identifier.pmid25587978-
dc.identifier.eissn1424-8220en_US
dc.identifier.rosgroupid2014003185-
dc.description.ros2014-2015 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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