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.
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Sanap, S.A., Nagori, M., Kshirsagar, V. (2011). Classification of Anemia Using Data Mining Techniques. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27242-4_14
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DOI: https://doi.org/10.1007/978-3-642-27242-4_14
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