A NOVEL SUBSET SELECTION FOR CLASSIFICATION OF DIABETE DATASET BY ITERATIVE METHODS

Authors

  • D.UDHAYAKUMARAPANDIAN Research Scholar, Department of Computer Science and Engineering. Annamalai University, Chidambaram-608 002, India,
  • RM. CHANDRASEKARAN Professor, Department of Computer Science and Engineering. Annamalai University, Chidambaram-608 002, India,
  • A.KUMARAVEL Professor and Dean, Department of Computer Science and Engineering. Bharath University, Selaiyur, Chennai-600073, India ,

Keywords:

Data mining ,Classification, Diabetes data set, Search Methods , Tree, Meta boost, Bayes

Abstract

Search methods applied to data mining techniques help us to analyze a data set. These methods are used  for reducing  the size of the search space in order to select the relevant attribute for identification of diabetes .The research community in diabetes is very much depends on practical prediction and classification of diabetes parameters based on qualified dataset. The main intention in this context is to deal with a large data set with high accuracy. For this purpose models are built using weka tool under supervised learning algorithm. It is necessary to reduce the data dimension before constructing the models and thus the search methods for selection of attributes are followed. Those models are to be applied to predict the possible test cases for evaluation

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Published

2014-09-30

How to Cite

D.UDHAYAKUMARAPANDIAN, RM. CHANDRASEKARAN, & A.KUMARAVEL. (2014). A NOVEL SUBSET SELECTION FOR CLASSIFICATION OF DIABETE DATASET BY ITERATIVE METHODS. International Journal of Pharma and Bio Sciences, 5(4), 9–18. Retrieved from https://ijpbs.in/index.php/journal/article/view/3321

Issue

Volume 5, Issue 3, 2014

Section

Research Articles

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License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.