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Advances in Fuzzy Systems
Volume 2008, Article ID 528461, 13 pages
Research Article

Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

1Information Technology Department, Faculty of Computer and Information, Cairo University, 5 Ahmed Zewal Street, Orman, Giza 12613, Egypt
2Quantitative and Information System Department, College of Business Administration, Kuwait University, P.O. Box 5468, Safat 13055, Kuwait
3Quantitative Methods and Information Systems Department, College of Business Administration, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
4Department of Solar and Space Research, National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, Egypt

Received 29 May 2007; Revised 10 October 2007; Accepted 18 November 2007

Academic Editor: Ajith Abraham

Copyright © 2008 Aboul ella Hassanien et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.