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Carpal Tunnel Syndrome automatic classification: electromyography vs. ultrasound imaging

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  • Maurizio Maravalle
  • Federica Ricca
  • Bruno Simeone
  • Vincenzo Spinelli

Abstract

We study automatic classification for the diagnosis of the Carpal Tunnel Syndrome (CTS), a disease frequently observed in occupational medicine. We apply different classification techniques to two real-life medical data sets related to a group of patients reporting the typical symptoms of this syndrome. We are particularly interested in the performance of “Box-Clustering” (BC), a method that is able to favor readability and interpretation of the results by medical doctors, thanks to its “box-type” output which naturally configures as a medical report. Preliminary results of a basic implementation of BC applied to different data sets already exist in the literature, and here we add more. In particular, in this paper, we apply a recently developed (and specialized) implementation of BC, and we test it for the first time on real-life medical data related to the CTS. Our purpose is to evaluate the performance of BC for automatic diagnosis, as well as, gain in explanation capability and interpretability. This is, in fact, a crucial aspect in medical applications that generally represents a limit for other well-known and powerful classification techniques. Copyright Sociedad de Estadística e Investigación Operativa 2015

Suggested Citation

  • Maurizio Maravalle & Federica Ricca & Bruno Simeone & Vincenzo Spinelli, 2015. "Carpal Tunnel Syndrome automatic classification: electromyography vs. ultrasound imaging," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 100-123, April.
    • Handle: RePEc:spr:topjnl:v:23:y:2015:i:1:p:100-123
    DOI: 10.1007/s11750-014-0325-0
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    References listed on IDEAS

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    1. Emilio Carrizosa & Belen Martin-Barragan & Dolores Romero Morales, 2010. "Binarized Support Vector Machines," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 154-167, February.
    2. Peter Hammer & Tibérius Bonates, 2006. "Logical analysis of data—An overview: From combinatorial optimization to medical applications," Annals of Operations Research, Springer, vol. 148(1), pages 203-225, November.
    3. Sorin Alexe & Eugene Blackstone & Peter Hammer & Hemant Ishwaran & Michael Lauer & Claire Pothier Snader, 2003. "Coronary Risk Prediction by Logical Analysis of Data," Annals of Operations Research, Springer, vol. 119(1), pages 15-42, March.
    4. Carrizosa, Emilio & Martín-Barragán, Belén & Morales, Dolores Romero, 2011. "Detecting relevant variables and interactions in supervised classification," European Journal of Operational Research, Elsevier, vol. 213(1), pages 260-269, August.
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