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Feature selection on chronic pain self reporting data

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Huang, Yan, Zheng, Huiru, Nugent, Christopher, McCullagh, Paul, Black, Norman, Vowles, Kevin and McCracken, Lance (2009) Feature selection on chronic pain self reporting data. In: Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference, Cyprus. IEEE. 4 pp. [Conference contribution]

Full text not available from this repository.

URL: http://dx.doi.org/10.1109/ITAB.2009.5394419

DOI: doi:10.1109/ITAB.2009.5394419

Abstract

Chronic pain is a common long-term condition that changes patients' physical and emotional functioning. Currently, the integrated biopsychosoical approach is the mainstay treatment for patients with chronic pain. Self reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. Nevertheless, a large number of questions (for example 329 questions in this study) may be required and as such may be viewed as not being convenient for patients to complete. This paper has applied the theory of information gain to rank the questions in addition to investigating important factors related to the treatment outcome. Analysis within the study ranked the questions from 1 to 329 based on information gain (highest to lowest). Results showed that questions related to chronic pain coping strategies and value-based actions had high information gain. Four supervised learning classifiers were used to investigate the correlations between feature numbers and classification accuracy. The results showed classifier that a multi-layer perceptron classifier obtained the highest classification accuracy (96.05%) on an optimized subset which consisted of 133 questions.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Smart Environments
ID Code:15826
Deposited By:Dr Huiru Zheng
Deposited On:29 Sep 2010 11:56
Last Modified:27 Jun 2011 11:29

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