Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients
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...
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| Format: | Article |
| Language: | English |
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Wiley
2008-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2008/528461 |
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| author | Aboul ella Hassanien Mohamed E. Abdelhafez Hala S. Own |
| author_facet | Aboul ella Hassanien Mohamed E. Abdelhafez Hala S. Own |
| author_sort | Aboul ella Hassanien |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ee01a7dbabe64d4fbb890377b9818011 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2008-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-ee01a7dbabe64d4fbb890377b98180112025-02-03T06:00:40ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2008-01-01200810.1155/2008/528461528461Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children PatientsAboul ella Hassanien0Mohamed E. Abdelhafez1Hala S. Own2Information Technology Department, Faculty of Computer and Information, Cairo University, 5 Ahmed Zewal Street, Orman, Giza 12613, EgyptQuantitative Methods and Information Systems Department, College of Business Administration, Kuwait University, P.O. Box 5969, Safat 13060, KuwaitDepartment of Solar and Space Research, National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, EgyptThe 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.http://dx.doi.org/10.1155/2008/528461 |
| spellingShingle | Aboul ella Hassanien Mohamed E. Abdelhafez Hala S. Own Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients Advances in Fuzzy Systems |
| title | Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients |
| title_full | Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients |
| title_fullStr | Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients |
| title_full_unstemmed | Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients |
| title_short | Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients |
| title_sort | rough sets data analysis in knowledge discovery a case of kuwaiti diabetic children patients |
| url | http://dx.doi.org/10.1155/2008/528461 |
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