Evaluation of three machine learning models for self-referral decision support on low back pain in primary care.

Journal: International journal of medical informatics
Published Date: Nov 23, 2017

Abstract

BACKGROUND: Most people experience low back pain (LBP) at least once in their life and for some patients this evolves into a chronic condition. One way to prevent acute LBP from transiting into chronic LBP, is to ensure that patients receive the right interventions at the right moment. We started research in the design of a clinical decision support system (CDSS) to support patients with LBP in their self-referral to primary care. For this, we explored the possibilities of using supervised machine learning. We compared the performances of the three classification models - i.e. 1. decision tree, 2. random forest, and 3. boosted tree - to get insight in which model performs best and whether it is already acceptable to use this model in real practice.

Authors

  • Wendy Oude Nijeweme-d'Hollosy
    University of Twente, CTIT, MIRA, EWI/BSS Telemedicine, Enschede, The Netherlands. Electronic address: w.dhollosy@utwente.nl.
  • Lex van Velsen
    University of Twente, CTIT, MIRA, EWI/BSS Telemedicine, Enschede, The Netherlands; Roessingh Research and Development, Telemedicine cluster, Enschede, The Netherlands.
  • Mannes Poel
    University of Twente, EWI/Human Media Interaction, The Netherlands.
  • Catharina G M Groothuis-Oudshoorn
    MIRA, Health Technology and Services Research University of Twente, Enschede, The Netherlands.
  • Remko Soer
    University of Groningen, University Medical Centre Groningen, Groningen Spine Centre, Groningen, The Netherlands; Saxion University of Applied Science, Enschede, The Netherlands.
  • Hermie Hermens
    University of Twente, CTIT, MIRA, EWI/BSS Telemedicine, Enschede, The Netherlands; Roessingh Research and Development, Telemedicine cluster, Enschede, The Netherlands.

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