Advertisement

Rule based classifiers for diagnosis of mechanical ventilation in Guillain-Barré Syndrome

  • José Hernández-Torruco
  • Juana Canul-Reich
  • David Lázaro Román
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 620)

Abstract

Breathing difficulty is a complication present in almost a third of Guillain-Barré Syndrome (GBS) patients. To alleviate this condition a mechanical respiratory device is needed. Anticipating this need is crucial for patients’ recovery. This can be achieved by means of machine learning predictive models. We investigated whether clinical, serological, and nerve conduction features separately can predict the need of mechanical ventilation with high accuracy. In this work, three rule based classifiers are applied to create a diagnostic model for this necessity. JRip, OneR and PART algorithms are analyzed using a real dataset. We performed classification experiments using train-test evaluation scheme. Clinical features were found as the best predictors.

Keywords

Data mining and processing JRip OneR PART train-test performance evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1. W.W. Cohen. Fast effective rule induction. In In Proceedings of the Twelfth International Conference on Machine Learning, pages 115–123. Morgan Kaufmann, 1995.Google Scholar
  2. 2. Fourrier F., Robriquet L., Hurtevent J.F., and Spagnolo S. A simple functional marker to predict the need for prolonged mechanical ventilation in patients with guillain- barr syndrome. Critical Care, 15: R65, 2011.Google Scholar
  3. 3. J. Han, M. Kamber, and J. Pei. Data mining: concepts and techniques. Morgan Kaufmann, San Francisco, USA, 2012.Google Scholar
  4. 4. Satoshi Kuwabara. Guillain-barré syndrome. Drugs, 64(6):597–610, 2004.Google Scholar
  5. 5. M. Matsumoto and T. Nishimura. Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul., 8(1):3–30, January 1998.Google Scholar
  6. 6. B.S. Paul, R. Bhatia, K. Prasad, M.V. Padma, M. Tripathi, and M.B. Singh. Clinical predictors of mechanical ventilation in guillain-barre syndrome. Neurol India, 60(2):150–3, Mar-Apr 2012.Google Scholar
  7. 7. E. Sohara et al. Guillain-barr syndrome in two patients with respiratory failure and a review of the japanese literature. Journal of Thoracic Disease, 4(6):601–607, 2012.Google Scholar
  8. 8. I.H. Witten, E. Frank, and M. A. Hall. Data Mining: Practical Machine Learning Tools and techniques. Morgan Kaufmann, third edition, 2011.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • José Hernández-Torruco
    • 1
  • Juana Canul-Reich
    • 1
  • David Lázaro Román
    • 1
  1. 1.División Académica de Informática y Sistemas.Universidad Juárez Autónoma de TabascoCunduacanMexico

Personalised recommendations