A comparison of missing value imputation methods for classifying patient outcome following trauma injury.

Penny, Kay I and Chesney, Thomas (2008) A comparison of missing value imputation methods for classifying patient outcome following trauma injury. In: Information Technology Interfaces 2008, 23-26 June 2008, Dubrovnik.

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A study is designed to compare several missing value imputation methods to enable classification of patient outcome following trauma injury. The Glasgow coma score is a measure of head injury severity, and is known to be important in determining patient outcome. The Glasgow coma scores are missing for 12% of the dataset, and in order to classify patient outcome for these patients, the missing values are first imputed. The first part of the study is designed to compare the performance of several missing value imputation methods, and errors between imputed values and known values of Glasgow coma scores are calculated. The second part of the study involves analysing the imputed data sets using logistic regression to classify whether patients live or die. Accuracy of results are compared in terms of sensitivity, specificity, positive predictive value and negative predictive value.

Item Type: Conference or Workshop Item (Paper)
Print ISSN: 1330-1012
ISBN: 978-953-7138-12-7
Additional Information: This paper appears in: 30th International Conference on Information Technology Interfaces, 2008. ITI 2008. “© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” DOI: 10.1109/ITI.2008.4588437
Uncontrolled Keywords: Logistic regression; missing value imputation; trauma injury;
University Divisions/Research Centres: The Business School > School of Management
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
600 Technology > 610 Medicine & health > 610.7 Medical education, research & nursing
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 5388
Depositing User: Dr Kay Penny
Date Deposited: 15 Jun 2012 10:41
Last Modified: 23 Sep 2015 10:52

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