A probabilistic SVM based decision system for pain diagnosis

Authors:
Yang Jinglin;Han-Xiong Li;Hu Yong
Affiliations:
Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China;Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam Road, Hong Kong, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Low back pain (LBP) affects a large proportion of the population and is the main cause of work disabilities worldwide. The mechanism of LBP remains largely unknown and many existing clinical treatment of LBP may be not effective to individual patients. Thus the diagnosis and treatment evaluation is crucial for LBP patients. Probabilistic support vector machine (PSVM) decision system is proposed in this article to deal with the diagnosis and treatment evaluation of LBP. The decision system consists of qualitative knowledge model and quantitative model. Expert knowledge and clinical experience are integrated into the design. To deal with the uncertainties in patients samples, PSVM is employed to learn the decision rules from data. The proposed decision system is applied to LBP patients and achieves better performance than the original system.