A comparative evaluation of data mining classification techniques on medical trauma data

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dc.contributor.advisorWegner, Trevoren_ZA
dc.contributor.authorRamaboa, Kutlwano K K Men_ZA
dc.date.accessioned2014-08-02T15:15:41Z
dc.date.available2014-08-02T15:15:41Z
dc.date.issued2004en_ZA
dc.descriptionIncludes bibliographical references (leaves 109-113).
dc.description.abstractThe purpose of this research was to determine the extent to which a selection of data mining classification techniques (specifically, Discriminant Analysis, Decision Trees, and three artifical neural network models - Backpropogation, Probablilistic Neural Networks, and the Radial Basis Function) are able to correctly classify cases into the different categories of an outcome measure from a given set of input variables (i.e. estimate their classification accuracy) on a common database.en_ZA
dc.identifier.apacitationRamaboa, K. K. K. M. (2004). <i>A comparative evaluation of data mining classification techniques on medical trauma data</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/5973en_ZA
dc.identifier.chicagocitationRamaboa, Kutlwano K K M. <i>"A comparative evaluation of data mining classification techniques on medical trauma data."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2004. http://hdl.handle.net/11427/5973en_ZA
dc.identifier.citationRamaboa, K. 2004. A comparative evaluation of data mining classification techniques on medical trauma data. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Ramaboa, Kutlwano K K M AB - The purpose of this research was to determine the extent to which a selection of data mining classification techniques (specifically, Discriminant Analysis, Decision Trees, and three artifical neural network models - Backpropogation, Probablilistic Neural Networks, and the Radial Basis Function) are able to correctly classify cases into the different categories of an outcome measure from a given set of input variables (i.e. estimate their classification accuracy) on a common database. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - A comparative evaluation of data mining classification techniques on medical trauma data TI - A comparative evaluation of data mining classification techniques on medical trauma data UR - http://hdl.handle.net/11427/5973 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5973
dc.identifier.vancouvercitationRamaboa KKKM. A comparative evaluation of data mining classification techniques on medical trauma data. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2004 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5973en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherStatistical Scienceen_ZA
dc.titleA comparative evaluation of data mining classification techniques on medical trauma dataen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMBusScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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