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Classification of Anemia Using Data Mining Techniques

  • Shilpa A. Sanap
  • Meghana Nagori
  • Vivek Kshirsagar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)

Abstract

The extraction of hidden predictive information from large databases is possible with data mining. Anemia is the most common disorder of the blood. Anemia can be classified in a variety of ways, based on the morphology of RBCs, etiology, etc . In this paper we present an analysis of the prediction and classification of anemia in patients using data mining techniques. The dataset constructed from complete blood count test data from various hospitals. We have worked out with classification method C4.5 decision tree algorithm and Support vector machine which are implemented as J48 and SMO(sequential minimal optimization) in Weka. Several experiments are conducted using these algorithms. The decision ree for classification of anemia is generated which gives best possible classification of anemia based on CBC reports along with severity of anemia. We have observed that C4.5 algorithm has best performance with highest accuracy.

Keywords

Support Vector Machine Iron Deficiency Anemia Data Mining Technique Decision Tree Algorithm Sequential Minimal Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Razali, A.M., Ali, S.: Generating Treatment Plan in Medicine: A Data Mining Approach. American Journal of Applied Sciences 6(2), 345–351 (2009), ISSN 1546- 9239 © 2009 Science PublicationsCrossRefGoogle Scholar
  2. 2.
    Schmaier, A.H., Petruzzelli, L.M.: Hematology for the medical student. Lippincott Wiliams and Wilkins 25Google Scholar
  3. 3.
    Bernadette, F.R., Doig, K.: Hematology: Clinical features & applications, 3rd edn., pp. 227–230Google Scholar
  4. 4.
    Ed Uthman’s homepage; Anemia Pathophysiologic Consequences, Classification, and Clinical Investigation (2009), http://web2.airmail.net/uthman/anemia/anemia.html
  5. 5.
    Fischbach, F.T.: A manual of laboratory & diagnostic tests, 6th edn. (2008)Google Scholar
  6. 6.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. (2001)Google Scholar
  7. 7.
  8. 8.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Fransisco (2005)zbMATHGoogle Scholar
  9. 9.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (2003)Google Scholar
  10. 10.
    Cadez, L.V., MacLaren, C.E., Smyth, P., McLachlan, G.J.: Hierarchical model for screening Iron Deficiency Anemia. Technical report no 99-14, Department of Information and Computer Science, University of California, IrvineGoogle Scholar
  11. 11.
    Wood, M.E., Philips, G.K.: Hematology/oncology secrets, 3rd edn., pp. 20–21Google Scholar
  12. 12.
    Medical Technology; RBC indices and anemia classification, http://www.irvingcrowley.com/cls/anemia.htm
  13. 13.
    Practical Utilization of the Complete Blood Count. Joseph M. Flynn, D.O.,MPH, FACP. Division Hematology-Oncology. THE Ohio State University, Columbus, OH (April 2008), sciocountrymedicalsociety.org/documents/CBC_Flynn.PPT
  14. 14.
    Ravel, R.: Clinical laboratory medicine: Clinical application of laboratory data, 6th edn., pp. 13–14 (1993)Google Scholar
  15. 15.
    Weka: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/
  16. 16.
    Beck, W.S.: Hematology, 5th edn., pp. 604–613Google Scholar
  17. 17.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shilpa A. Sanap
    • 1
  • Meghana Nagori
    • 2
  • Vivek Kshirsagar
    • 2
  1. 1.Department of Computer Science and Engineering, Marathwada Institute of TechinologyDr. B.A.M. UniversityAurangabadIndia
  2. 2.Department of Computer Science and Engineering, Govt. Engineering CollegeDr. B.A.M. UniversityAurangabadIndia

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