Data Mining in the Study of the Chronic Obstructive Pulmonary Disease

  • Maribel Yasmina Santos
  • Jorge Cruz
  • Artur Teles de Araújo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7451)


Data Mining algorithms have been used to analyse huge amounts of data and extract useful models or patterns from the analysed data. Those models or patterns can be used to support the decision making process in organizations. In the health domain, and besides the support to the decision process, those algorithms are useful in the analysis and characterization of several diseases. This paper presents the particular case of the use of different Data Mining algorithms to support health care specialists in the analysis and characterization of symptoms and risk factors related with the Chronic Obstructive Pulmonary Disease. This is an airflow limitation that is not fully reversible and that affects up to one quarter of the adults with 40 or more years. For this specific study, data from 1.880 individuals were analysed with decision trees and artificial neural networks in order to identify predictive models for this disease. Clustering was used to identify groups of individuals, with the chronic obstructive pulmonary disease, presenting similar risk factors and symptoms. Furthermore, association rules were used to identify correlations among the risk factors and the symptoms. The results obtained so far are promising as several models confirm the difficulties that are normally associated to the diagnosis of this disease and point to characteristics that must be taken into account in its comprehension.


business intelligence data mining decision models chronic obstructive pulmonary disease 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    GOLD, Global Strategy for Diagnosis, Management, and Prevention of COPD. Technical Report, Global Initiative for Chronic Obstructive Lung Disease (2010)Google Scholar
  2. 2.
    Pauwels, R.A., Buist, A.S., Calverley, P.M.A., Jenkins, C.R., Hurd, S.S.: Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine 163, 1256–1276 (2001)Google Scholar
  3. 3.
    Dinis, R., Ribeiro, A., Santos, M.Y., Cruz, J., Araújo, A.T.D.: A Business Intelligence Infrastructure Supporting Respiratory Health Analysis. In: Proceedings of the First International Conference on Business Intelligence and Technology (BusTECH 2011), Rome, Italy (2011)Google Scholar
  4. 4.
    Buist, A.S., McBurnie, M.A., et al.: International variation in the prevalence of COPD (the BOLD study): a population-based prevalence study. Lancet 370(9589), 741–750 (2007)CrossRefGoogle Scholar
  5. 5.
    Lamprecht, B., McBurnie, M.A., et al.: COPD in never smokers: results from the population-based burden of obstructive lung disease study. Chest 139(4), 752–763 (2011)CrossRefGoogle Scholar
  6. 6.
    Cardoso, J., et al.: Prevalence of Chronic Obstructive Pulmonary Disease in Portugal. Am J. Resp. Crit. Care Med. 167(7), 23 (2003)Google Scholar
  7. 7.
    Borges, M., Gouveia, M., et al.: Carga de Doença atribuível ao tabagismo em Portugal. Rev. Port. Pneumol. 15(6), 951–1004 (2009) (in Portuguese)Google Scholar
  8. 8.
    Bárbara, C., Rodrigues, F., et al.: COPD Prevalence in Portugal. The Burden of Obstructive Lung Disease Study. In: European Respiratory Society Annual Congress, Barcelona (2010)Google Scholar
  9. 9.
    RONDR, Relatório do Observatório Nacional das Doenças Respiratórias. Portuguese Lung Foundation. pp. 142–149 (2010) (in Portuguese)Google Scholar
  10. 10.
    Esteban, C., Arostegui, I., et al.: Development of a decision tree to assess the severity and prognosis of stable COPD. European Respiratory Journal 38(6), 1294–1300 (2011)CrossRefGoogle Scholar
  11. 11.
    Paoletti, M., Camiciottoli, G., et al.: Explorative data analysis techniques and unsupervised clustering methods to support clinical assessment of Chronic Obstructive Pulmonary Disease (COPD) phenotypes. Journal of Biomedical Informatics 42, 1013–1021 (2009)CrossRefGoogle Scholar
  12. 12.
    Weatherall, M., Shirtcliffe, P., Travers, J., Beasley, R.: Use of cluster analysis to define COPD phenotypes. European Respiratory Journal 36(3), 472–474 (2010)CrossRefGoogle Scholar
  13. 13.
    Fayyad, U., Uthurusamy, R.: Data Mining and Knowledge Discovery in Databases. Communications of the ACM 39(11), 24–26 (1996)CrossRefGoogle Scholar
  14. 14.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers (2001)Google Scholar
  15. 15.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 1–34. The MIT Press, Massachusetts (1996)Google Scholar
  16. 16.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  17. 17.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD Conference on Management of data, Washington, DC (1993)Google Scholar
  18. 18.
    Zaït, M., Messatfa, H.: A comparative study of clustering methods. Future Generation Computer Systems 13(2), 149–159 (1997)CrossRefGoogle Scholar
  19. 19.
    Cios, K., Pedrycz, W., Swiniarski, R.: Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers (1998)Google Scholar
  20. 20.
    Groth, R.: Data Mining: Building Competitive Advantage. Prentice Hall PTR (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maribel Yasmina Santos
    • 1
  • Jorge Cruz
    • 2
  • Artur Teles de Araújo
    • 3
  1. 1.Information Systems Department, Algoritmi Research CentreUniversity of MinhoPortugal
  2. 2.Faculty of MedicineUniversity of LisbonPortugal
  3. 3.Portuguese Lung FoundationPortugal

Personalised recommendations