Advertisement

Identification of School-Aged Children with High Probability of Risk Behavior on the Basis of Easily Measurable Variables

  • Peter Koncz
  • Jan Paralic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7058)

Abstract

The use of the methods of Knowledge Discovery in Databases (KDD) in the domain of public health is still topical. One of the major reasons for its increasing use is the need for an efficient processing of the increasing volumes of data. The aim of our contribution is to analyze the possibilities of the usage of these methods to identify the groups of school-aged children with a high probability of risky behavior. The obtained results are useful for the formation of models applicable for more efficient identification of target groups of prevention programs. In this work we use Slovak national dataset from the international study Health Behaviour in School-Aged Children. The used machine learning methods were Support Vector Machine, Naïve Bayes Classifier and the J48 machine learning algorithm. The results suggest promising possibilities for the use of the machine learning methods to develop classification models useful for public health.

Keywords

Knowledge discovery in databases machine learning public health risky behavior 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Li, J., Fu, A.W.-c., Fahey, P.: Efficient discovery of risk patterns in medical data. Artificial intelligence in Medicine 45, 77–89 (2009)CrossRefGoogle Scholar
  2. 2.
    Sibbritt, D., Gibberd, R.: The Effective Use of a Summary Table and Decision Tree Methodology to Analyze Very Large Healthcare Datasets. Health Care Management Science 7, 163–171 (2004)CrossRefGoogle Scholar
  3. 3.
    Expert Health Data Programming, http://www.ehdp.com/links/index.htm
  4. 4.
    Holmes, J.H., Durbin, D.R., Winston, F.K.: Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research. In: Proc. AMIA Symp., pp. 359–363 (2000)Google Scholar
  5. 5.
    Orlygsdottir, B.: Using knowledge discovery to identify potentially useful patterns of health promotion behavior of 10–12 year old Icelandic children. The University of Iowa (2008)Google Scholar
  6. 6.
    Flouris, A.D., Duffy, J.: Applications of Artificial Intelligence Systems in the Analysis of Epidemiological Data. European Journal of Epidemiology 21, 167–170 (2006)CrossRefGoogle Scholar
  7. 7.
    Poynton, M.R., McDaniel, A.M.: Classification of smoking cessation status with a backpropagation neural network. Journal of Biomedical Informatics 39, 680–686 (2006)CrossRefGoogle Scholar
  8. 8.
    Lemon, S., Roy, J., Clark, M., Friedmann, P., Rakowski, W.: Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression. Annals of Behavioral Medicine 26, 172–181 (2003)CrossRefGoogle Scholar
  9. 9.
    MacDowell, M., Somoza, E., Rothe, K., Fry, R., Brady, K., Bocklet, A.: Understanding birthing mode decision making using artificial neural networks. Medical Decision Making 21, 433–443 (2001)CrossRefGoogle Scholar
  10. 10.
    Goodwin, L.K., Iannacchione, M.A., Hammond, W.E., Crockett, P., Maher, S., Schlitz, K.: Data Mining Methods Find Demographic Predictors of Preterm Birth. Nursing Research 50, 340–345 (2001)CrossRefGoogle Scholar
  11. 11.
    Bertsimas, D., Bjarnadóttir, M.V., Kane, M.A., Kryder, J.C., Pandey, R., Vempala, S., Wang, G.: Algorithmic Prediction of Health-Care Costs. Operations Research 56, 1382–1392 (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Yu, W., Liu, T., Valdez, R., Gwinn, M., Khoury, M.: Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Medical Informatics and Decision Making 10, 16 (2010)CrossRefGoogle Scholar
  13. 13.
    Health Behaviour in School-Aged Children, http://www.hbsc.org/index.html
  14. 14.
    Vapnik, V.: The nature of statistical learning theory. Springer-Verlag New York, Inc. (1995)Google Scholar
  15. 15.
    Abe, S.: Support Vector Machines for Pattern Classification (Advances in Pattern Recognition). Springer-Verlag New York, Inc. (2005)Google Scholar
  16. 16.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)CrossRefGoogle Scholar
  17. 17.
    Paralic, J., Furdik, K., Tutoky, G., Bednar, P., Sarnovsky, M., Butka, P., Babic, F.: Dolovanie znalostí z textov. Equilibria, s.r.o. (2010)Google Scholar
  18. 18.
    Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc. (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peter Koncz
    • 1
  • Jan Paralic
    • 1
  1. 1.Faculty of Electrical Engineering and Informatics/Dept. of Cybernetics and Artificial IntelligenceTechnical University of KosiceKosiceSlovakia

Personalised recommendations