Rough Classifier: Experiments on Two Medical Data Sets

Azuraliza A. Bakar, Md N. Sulaiman, Mohamed Othman and Mohd H. Selamat
* Author for corresponding; e-mail address: -
Volume: Vol.28 No.1 (JUNE 2001)
Opinion
DOI:
Received: 28 Febuary 2001, Revised: -, Accepted: 10 April 2001, Published: -

Citation: Bakar A.A., Sulaiman M.N., Othman M. and Selamat M.H., Rough Classifier: Experiments on Two Medical Data Sets, Chiang Mai Journal of Science, 2001; 28(1): 59-63.

Abstract

The study examines the Rough Set approach in solving the classification problems in medical dataset. The methodology is concerned with the classificatory analysis of imprecise, uncertain or incomplete information or knowledge expressed in terms of data acquired from experience. The algorithm is experimented with two medical datasets, Lymphography and Heart Disease datasets. The experimental results are compared with another two learning systems, neural network and multiple regression. It is shown that good classification model can be obtained using the rough set approach. The results indicate that the rough modeling is a promising classification method in solving the decision making in medical classification problem.

Keywords: rough set theory, decision system, reduct, classification accuracy

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