Classification Rule Generation for Diabetic Patients using Rough Set Approach

Authors

  • Ghosh K University Institute of Technology The University of Burdwan Burdwan, India
  • Mukhopadhyay S Department of Computer Science The University of Burdwan Burdwan, India

Keywords:

Data Mining, Rough Set, Indiscernibility, Reduct, Decision tables and decision algorithms, Classification Rule

Abstract

Classification rule-generation is a Data Mining activity. A supervised process uses a training data set to generate the rules. The objective is to predict a predefined class or goal attribute, which can never appear in the antecedent part of a rule. The generated rules are used to predict the class attribute of an unknown test data set. In this paper we have tried to generate classification rule for diabetic patients using Rough set. This present research work relates Data mining to Health Informatics. The proposed algorithm generates the different classification rules related to predict insulin dose depending upon blood glucose measurement and helps in diabetes monitoring.

References

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Michael Kahn, “ DIABETES data sets”, AIM-94 data set, Washington University, St. Louis, MO, 1994.

Klingensmith, GJ., American Diabetes Association, Intensive Diabetes Management, Third Edition, p. 107, 2003.

National Library of Medicine, http://www.nlm.nih.gov/tsd/acquisitions/cdm/subjects58.html.

https://archive.ics.uci.edu/ml/datasets/Diabetes

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Published

2015-02-28

How to Cite

[1]
K. Ghosh and S. Mukhopadhyay, “Classification Rule Generation for Diabetic Patients using Rough Set Approach”, Int. J. Comp. Sci. Eng., vol. 3, no. 1, pp. 88–96, Feb. 2015.

Issue

Vol. 3 No. 1 (2015): IJCSE Special Issue February Edition

Section

Research Article

License

Copyright (c) 2025 Ghosh K, Mukhopadhyay S

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