Classification of Anemia Using Data Mining Techniques

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


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.


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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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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