Machine Learning Tool for Automatic ASA Detection

  • Mohammed El Amine Lazouni
  • Mostafa El Habib Daho
  • Nesma Settouti
  • Mohammed Amine Chikh
  • Saïd Mahmoudi
Part of the Studies in Computational Intelligence book series (SCI, volume 488)

Abstract

The application of machine learning tools has shown its advantages in medical aided decision. This paper presents the implementation of three supervised learning algorithms: the C4.5 decision tree classifier, the Support Vector Machines (SVM) and the Multilayer Perceptron MLP’s in MATLAB environment, on the preoperative assessment database. The classification models were trained using a new database collected from 898 patients, each of whom being represented by 17 features and included in one among 4 classes. The patients in this database were selected from different private clinics and hospitals of western Algeria.In this paper, the proposed system is devoted to the automatic detection of some typical features corresponding to the American Society of Anesthesiologists sores (ASA scores). These characteristics are widely used by all Doctors Specialized in Anesthesia (DSA’s) in pre-anesthesia examinations. Moreover, the robustness of our system was evaluated using a 10-fold cross-validation method and the results of the three proposed classifiers were compared.

Keywords

ASA score DSA Pre-anesthesia consultation SVM MLPs C4.5 

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References

  1. 1.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press (2000)Google Scholar
  2. 2.
    Hussman, J., Russell, R.: Memorix: Surgery. Tech. rep., p. 66. Chapman & Hall Medical (1997)Google Scholar
  3. 3.
    Karpagavalli, S., Jamuna, K., Vijaya, M.: Machine learning approach for pre-operative anaesthetic risk prediction. International Journal of Recent Trends in Engineering 1(2) (May 2009)Google Scholar
  4. 4.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: The Fourteenth International Joint Conference on Artificial Intelligence IJCAI (1995) ISSN 1137-1143 Google Scholar
  5. 5.
    Lazouni, M.A., El Habib Daho, M., Chikh, M.A.: Un système multi-agent pour l aide au diagnostic en anesthésie. In: Biomedical Engineering International Conference (BIOMEIC 2012), Tlemcen, Algeria, October 10-11, p. 82 (2012) ISSN 2253-0886Google Scholar
  6. 6.
    Lutz, P.K.: The medical algorithms project, ch31. anaesthesiology, section: Pre-operative patient classification and preparation. Online Excel 681-687 (2008)Google Scholar
  7. 7.
    Sakland, M.: Grading of patients for surgical procedures. Anesthesiology 2, 281–284 (1941)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mohammed El Amine Lazouni
    • 1
  • Mostafa El Habib Daho
    • 1
  • Nesma Settouti
    • 1
  • Mohammed Amine Chikh
    • 1
  • Saïd Mahmoudi
    • 2
  1. 1.Biomedical Engineering LaboratoryTlemcenAlgeria
  2. 2.Computer Science Department - Faculty of EngineeringUniversity of MonsMonsBelgium

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