Danso, SO, Atwell, ES and Johnson, O (2013) A comparative study of machine learning methods for verbal autopsy text classification. IJCSI International Journal of Computer Science Issues, 10 (6). ISSN 1694-0784
Abstract
A Verbal Autopsy is the record of an interview about the circumstances of an uncertified death. In developing countries, if a death occurs away from health facilities, a field-worker interviews a relative of the deceased about the circumstances of the death; this Verbal Autopsy can be reviewed offsite. We report on a comparative study of the processes involved in Text Classification applied to classifying Cause of Death: feature value representation; machine learning classification algorithms; and feature reduction strategies in order to identify the suitable approaches applicable to the classification of Verbal Autopsy text. We demonstrate that normalised term frequency and the standard TFiDF achieve comparable performance across a number of classifiers. The results also show Support Vector Machine is superior to other classification algorithms employed in this research. Finally, we demonstrate the effectiveness of employing a ’locally-semisupervised’ feature reduction strategy in order to increase performance accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2013 International Journal of Computer Science Issues. This is an author produced version of a paper published in International Journal of Computer Science Issues. Uploaded in accordance with the publisher's self-archiving policy |
Keywords: | Text classification; verbal autopsy; machine learning; algorithms; term weighting; feature reduction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jun 2014 14:45 |
Last Modified: | 19 Jan 2018 15:35 |
Status: | Published |
Publisher: | IJCSI Press |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79253 |