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Data mining trauma injury data using C5.0 and logistic regression to determine factors associated with death.

Chesney, Thomas, Penny, Kay I, Chesney, David, Oakley, Peter, Viglas, S, Templeton, John and Maffulli, Nicola (2009) Data mining trauma injury data using C5.0 and logistic regression to determine factors associated with death. International Journal of Healthcare Technology and Management, 10 (1/2). pp. 16-26. ISSN 1368 2156

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Abstract/Description

Trauma injury data collected over 10 years at a UK hospital are analysed. The data include injury details such as patient age and gender, the mechanism of injury, various measures of injury severity, management interventions, and treatment outcome. Logistic regression modelling was used to determine which factors were independently associated with death during hospital stay. The data mining algorithm C5.0 was also used to determine those factors in the data that can be used to predict whether a patient will live or die. Logistic modelling and C5.0 show that different subsets of injury severity scores, and patient age, are associated with survival. In addition, C5.0 also shows that gender, and whether the patient was referred from another hospital, is important. The two techniques give different insights into those factors associated with death after trauma

Item Type: Article
Print ISSN: 1368 2156
Uncontrolled Keywords: : C5.0 algorithm; data mining; logistic regression; trauma injury data; UK; United Kingdom; healthcare; patient age; patient gender; injury mechanism; injury severity; management interventions; treatment outcome; patient survival; death factors.
University Divisions/Research Centres: The Business School > School of Management
Dewey Decimal Subjects: 500 Science > 510 Mathematics > 519 Probabilities & applied mathematics
Library of Congress Subjects: Q Science > QA Mathematics
Item ID: 5105
Depositing User: Mr David Main
Date Deposited: 21 Mar 2012 14:11
Last Modified: 08 Jan 2013 11:07
URI: http://researchrepository.napier.ac.uk/id/eprint/5105

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