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Ensemble Classifier Systems for Headache Diagnosis

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 284)

Abstract

Headache, medically known as cephalalgia, may have a wide range of symptoms and its types may be related and mixed. Its proper diagnosis is difficult and automatic diagnosis is usually rather imprecise, therefore, the problem is still the focus of intensive research. In the paper we propose headache diagnosis method which makes the decision on the basis of questionnaire only. It distinguished among 11 headache classes, which taxonomy is provided. The paper presents results of experiments which aim at selecting the best classification algorithm including several classical machine learning methods as well as ensemble approach. Results of experiments carried on dataset collected in University of Novi Sad confirm that the automatic classification system can gain high accuracy of classification for the problem under consideration.

Keywords

headache diagnosis ensemble classifier systems 

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References

  1. 1.
    Liebowitz, J. (ed.): The Handbook of Applied Expert Systems. CRC Press (1998)Google Scholar
  2. 2.
    Kaplan, B.: Evaluating informatics applications - clinical decision support systems literature review. International Journal of Medical Informatics 64, 15–37 (2001)CrossRefGoogle Scholar
  3. 3.
    Simić, S., Simić, D., Cvijanović, M.: Clinical and socio-demographic characteristics of tension type headache in working population. HealthMED 6(4), 1341–1347 (2012)Google Scholar
  4. 4.
    Kuncheva, L.I.: Combining Pattern Classifiers. Methods and Algorithms. Wiley (2004)Google Scholar
  5. 5.
    Headache Classification Committee of the International Headache Society. Classification and diagnostic criteria for headache disorders, cranial neuralgias and facial pain. Cephalalgia 8(suppl. 7), 1–96 (1988)Google Scholar
  6. 6.
    Brown, M.R.: The classification and treatment of headache. Medical Clinics of North America 35(5), 1485–1493 (1951), PMID 14862569Google Scholar
  7. 7.
    Ad Hoc Committee on Classification of Headache. “Classification of Headache”. JAMA 179(9), 717–718 (1962), doi:10.1001/jama.1962.03050090045008Google Scholar
  8. 8.
    Vos, T.: Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380(9859), 2163–2196 (2012), doi:10.1016/S0140-6736(12)61729-2, PMID 23245607Google Scholar
  9. 9.
    MacGregor, E.A., Hackshaw, A.: Prevalence of migraine on each day of the natural menstrual cycle. Neurology 63(2), 351–353 (2004)CrossRefGoogle Scholar
  10. 10.
    John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995)Google Scholar
  11. 11.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 2(4), 303–314 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Annual ACM Workshop on COLT, pp. 144–152. ACM Press, Pittsburgh (1992)Google Scholar
  13. 13.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
  14. 14.
    Quinlan, J.R.: Induction of Decision Trees. In: Machine Learning, vol. 1, pp. 81–106. Kluwer Academic Publishers (1986)Google Scholar
  15. 15.
    Hall, M.A.: Correlation-base feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359–366 (2000)Google Scholar
  16. 16.
    Kohavi, R., John, G.: Wrappers for feature subset selection. Artficial intelligence 97(1-2), 273–324 (1997)CrossRefzbMATHGoogle Scholar
  17. 17.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389–422 (2002)CrossRefzbMATHGoogle Scholar
  18. 18.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.IT4InnovationsVSB-Technical University of OstravaOstravaCzech Republic
  2. 2.Department of Systems and Computer NetworksWrocław University of TechnologyWrocławPoland
  3. 3.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia
  4. 4.Faculty of MedicineUniversity of Novi SadNovi SadSerbia

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