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

Includes bibliographical references (leaves 109-113). === 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, Probabli...

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Bibliographic Details
Main Author:	Ramaboa, Kutlwano K K M
Other Authors:	Wegner, Trevor
Format:	Dissertation
Language:	English
Published:	University of Cape Town 2014
Subjects:	Statistical Science
Online Access:	http://hdl.handle.net/11427/5973

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spelling	ndltd-netd.ac.za-oai-union.ndltd.org-uct-oai-localhost-11427-59732020-10-06T05:10:48Z A comparative evaluation of data mining classification techniques on medical trauma data Ramaboa, Kutlwano K K M Wegner, Trevor Statistical Science Includes bibliographical references (leaves 109-113). 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. 2014-08-02T15:15:41Z 2014-08-02T15:15:41Z 2004 Master Thesis Masters MBusSc http://hdl.handle.net/11427/5973 eng application/pdf University of Cape Town Faculty of Science Department of Statistical Sciences
collection	NDLTD
language	English
format	Dissertation
sources	NDLTD
topic	Statistical Science
spellingShingle	Statistical Science Ramaboa, Kutlwano K K M A comparative evaluation of data mining classification techniques on medical trauma data
description	Includes bibliographical references (leaves 109-113). === 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.
author2	Wegner, Trevor
author_facet	Wegner, Trevor Ramaboa, Kutlwano K K M
author	Ramaboa, Kutlwano K K M
author_sort	Ramaboa, Kutlwano K K M
title	A comparative evaluation of data mining classification techniques on medical trauma data
title_short	A comparative evaluation of data mining classification techniques on medical trauma data
title_full	A comparative evaluation of data mining classification techniques on medical trauma data
title_fullStr	A comparative evaluation of data mining classification techniques on medical trauma data
title_full_unstemmed	A comparative evaluation of data mining classification techniques on medical trauma data
title_sort	comparative evaluation of data mining classification techniques on medical trauma data
publisher	University of Cape Town
publishDate	2014
url	http://hdl.handle.net/11427/5973
work_keys_str_mv	AT ramaboakutlwanokkm acomparativeevaluationofdataminingclassificationtechniquesonmedicaltraumadata AT ramaboakutlwanokkm comparativeevaluationofdataminingclassificationtechniquesonmedicaltraumadata
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A comparative evaluation of data mining classification techniques on medical trauma data

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