Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/9932
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Lee, WC | - |
dc.creator | Hung, FH | - |
dc.creator | Tsang, KF | - |
dc.creator | Tung, HC | - |
dc.creator | Lau, WH | - |
dc.creator | Rakocevic, V | - |
dc.creator | Lai, LL | - |
dc.date.accessioned | 2015-10-13T08:28:20Z | - |
dc.date.available | 2015-10-13T08:28:20Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/9932 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular 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.rights | The 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/s150101312 | en_US |
dc.subject | Analytic hierarchy process | en_US |
dc.subject | Cardiovascular diseases classifier | en_US |
dc.subject | Electrocardiogram | en_US |
dc.subject | Multiple criteria decision analysis | en_US |
dc.subject | Support vector machine | en_US |
dc.title | A speedy cardiovascular diseases classifier using multiple criteria decision analysis | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1312 | en_US |
dc.identifier.epage | 1320 | en_US |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.3390/s150101312 | en_US |
dcterms.abstract | Each 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors, Jan. 2015, v. 15, no. 1, p. 1312-1320 | - |
dcterms.isPartOf | Sensors | - |
dcterms.issued | 2015 | - |
dc.identifier.scopus | 2-s2.0-84937193736 | - |
dc.identifier.pmid | 25587978 | - |
dc.identifier.eissn | 1424-8220 | en_US |
dc.identifier.rosgroupid | 2014003185 | - |
dc.description.ros | 2014-2015 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Lee_Speedy_Cardiovascular_Diseases.pdf | 824.26 kB | Adobe PDF | View/Open |
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