Advertisement

Preprocessing of Automated Blood Cell Counter Data and Generation of Association Rules in Clinical Pathology

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

This paper applies the preprocessing phases of the Knowledge Discovery in Databases to the automated blood cell counter data and generates association rules using apriori algorithm. The functions of an automated blood cell counter from a clinical pathology laboratory and the phases in Knowledge Discovery in Databases are explained briefly. Twelve thousand records are taken from a clinical laboratory for processing. The preprocessing steps of the KDD process are applied on the blood cell counter data. This paper applies the Apriori algorithm on the blood cell counter data and generates interesting association rules that are useful for medical diagnosis.

Keywords

Clinical Pathology Blood Cell Counter Knowledge Discovery in Databases Data Mining Association Rule Mining Apriori algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers (2006)Google Scholar
  2. 2.
    Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Pearson Education (2007)Google Scholar
  3. 3.
    Automated Blood Cell Counter, http://www.medscape.com
  4. 4.
    Goh, D.H., Ang, R.P.: An Introduction to Association rule mining: An application in counseling and help-seeking behavior of adolescents. Behaviour Research Methods 39(2), 259–266 (2007)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Associations between Sets of Items in Large Databases. In: Proc. of the ACM-SIGMOD 1993 Int’l. Conference on Management of Data, pp. 207–216 (May 1993)Google Scholar
  6. 6.
    Duca, D.J.: Auto Verification in a Laboratory Information System. Laboratory Medicine 33(1), 21–25 (2002)CrossRefGoogle Scholar
  7. 7.
    Quillen, K., Murphy, K.: Quality Improvement to Decrease Spe-cimen Mislabeling in Transfusion Medicine. Archives of Pathology and Laboratory Medicine 130, 1196–1198 (2006)Google Scholar
  8. 8.
    Aslandogan Alp, Y., Mahajani, G.A.: Evidence Combination in Medical Data Mining. In: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC 2004), vol. 2, pp. 465–469 (2004)Google Scholar
  9. 9.
    Agrawal, R., Imielinski, T., Swami, A.: Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering 5(6), 914–925 (1993)CrossRefGoogle Scholar
  10. 10.
    Toussi, M., Lamy, J.-B., Le Toumelin, P., Venot, A.: Using data mining techniques to explore physicians’ therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes. BMC Medical Informatics and Decision Making, 9–28 (2009)Google Scholar
  11. 11.
    Dogan, S., Turkoglu, I.: Diagnosing Hyperlipidemia using Association rules. Mathematical and Computational Applications, Association for Scientific Research 13(3), 193–202 (2008)Google Scholar
  12. 12.
    Li, J., Fu, A.W.-C., He, H., et al.: Mining risk Patterns in Medical data. In: KDD 2005, Chicago, Illinois, USA, pp. 770–775 (2005)Google Scholar
  13. 13.
    Srikant, R., Agrawal, R.: Mining Generalized Association Rules. In: Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Swizerland (September 1995)Google Scholar
  14. 14.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile (September 1994)Google Scholar
  15. 15.
    Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools. In: SIGKDD Explorations, ACM SIGKDD (June 1999)Google Scholar
  16. 16.
    Cerrito, P., Cerrito, J.C.: Data and Text Mining the Electronic Medical Record to Improve Care and to Lower Costs. In: Proceedings of SUGI 31, March 26-29, pp. 77–31 (2006)Google Scholar
  17. 17.
    Cios, K.J., Moore, G.W.: Uniqueness of Medical Data Mining. Artificial Intelligence in Medicine 26(1-2), 1–24 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceMadras Christian CollegeChennaiIndia
  2. 2.Department of Computer Science and EngineeringAnna University of Technology MaduraiMaduraiIndia

Personalised recommendations