Classification of anemia using data mining techniques

  • Authors:

  • Shilpa A. Sanap;Meghana Nagori;Vivek Kshirsagar

  • Affiliations:

  • Department of Computer Science and Engineering, Marathwada Institute of Techinology, Dr. B.A.M. University, Aurangabad, Maharashtra, India;Department of Computer Science and Engineering, Govt. Engineering College, Dr. B.A.M. University, Aurangabad, Maharashtra, India;Department of Computer Science and Engineering, Govt. Engineering College, Dr. B.A.M. University, Aurangabad, Maharashtra, India

  • Venue:
  • SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
  • Year:
  • 2011

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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.