A probabilistic SVM based decision system for pain diagnosis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

24 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)9346-9351
Journal / PublicationExpert Systems with Applications
Volume38
Issue number8
Publication statusPublished - Aug 2011

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. © 2011 Published by Elsevier Ltd.

Research Area(s)

  • Decision making, Expert knowledge, Low back pain (LBP), Probabilistic support vector machine, Support vector machine

Citation Format(s)