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Title:
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CLASSIFICATION OF HEALTH LEVEL FROM CHRONIC PAIN SELF REPORTING |
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Author(s):
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Yan Huang , Huiru Zheng , Chris Nugent , Paul Mccullagh , Norman Black , Kevin Vowles , Lance Mccracken |
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ISBN:
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978-972-8924-81-2 |
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Editors:
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Mário Macedo |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Chronic pain; Self report; Classification. |
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Type:
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Full Paper |
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First Page:
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43 |
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Last Page:
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50 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper proposes an approach to identify patients health levels based on the information gathered following a process
of self reporting based on the patients current condition. The goal of approach is the accurate provision of information to
assist with self management of chronic pain. Four supervised classifiers, namely decision tree, naïve Bayes, support
vector machine and multilayer perceptron, have been applied to classify the health level of patients suffering from
chronic pain based on information collected from self reports from three treatment stages - pre-treatment stage, posttreatment
stage and 3-month follow-up stage. Three binary classification problems, i.e. pre-treatment vs. post-treatment,
pre-treatment vs. 3-month follow-up and post-treatment vs. 3-month follow-up, were investigated. The classification
accuracy and area under Receiver Operating Characteristics (ROC) curve ranged from 66.7% ~ 94.7% and 0.689 ~ 0.989
respectively. The multilayer perceptron classifier achieved the best performance with a classification accuracy of 94.7%
and area under ROC curve of 0.981 for the pre-treatment vs. post-treatment classification. The results from this study
have demonstrated that it is feasible to apply automated classification techniques to identify patients health level from
their self reports. This data may be used as an important indicator in automated approaches to chronic disease self
management, an area which is currently receiving much attention. Further work will investigate the presence of optimal
features derived from questionnaires to improve the classification performance. |
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