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Rough Set Theory Based Attribute Reduction for Breast Cancer Diagnosis


Affiliations
1 Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India
2 Department of Computer Science, Dr. Ambedkar Govt. Arts College, Chennai, Tamil Nadu, India
 

Data mining (DM) techniques are used to determine interesting patterns from different domains according to the need of applications and the analyst. Medical field is one among the major user of the mining technology for diagnosing the attributes for the medical issues. Breast cancer is one of the most important medical problems. The modern researchers and technological advancements attempted to determine the cause and prevention in an effective manner with lesser number of attributes. But the diagnosis is lengthy process with multiple and multilevel attribute analysis in certain cases. In order to improve the accuracy of diagnosis with limited attributes, in this paper rough set based relative reduct algorithm is used to reduce the number of attributes using equivalence relation. The effectiveness of proposed Rough Set Reduction algorithm is analyzed on Wisconsin Breast Cancer Dataset (WBCD) and presented as a part of the paper. The experimental results show that the relative reduct performs better attribute reduction.

Keywords

Data mining, Data Preprocessing, Rough Set, Data reduction, Breast Cancer Diagnosis
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Abstract Views: 359

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  • Rough Set Theory Based Attribute Reduction for Breast Cancer Diagnosis

Abstract Views: 359  |  PDF Views: 75

Authors

T. Sridevi
Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India
A. Murugan
Department of Computer Science, Dr. Ambedkar Govt. Arts College, Chennai, Tamil Nadu, India

Abstract


Data mining (DM) techniques are used to determine interesting patterns from different domains according to the need of applications and the analyst. Medical field is one among the major user of the mining technology for diagnosing the attributes for the medical issues. Breast cancer is one of the most important medical problems. The modern researchers and technological advancements attempted to determine the cause and prevention in an effective manner with lesser number of attributes. But the diagnosis is lengthy process with multiple and multilevel attribute analysis in certain cases. In order to improve the accuracy of diagnosis with limited attributes, in this paper rough set based relative reduct algorithm is used to reduce the number of attributes using equivalence relation. The effectiveness of proposed Rough Set Reduction algorithm is analyzed on Wisconsin Breast Cancer Dataset (WBCD) and presented as a part of the paper. The experimental results show that the relative reduct performs better attribute reduction.

Keywords


Data mining, Data Preprocessing, Rough Set, Data reduction, Breast Cancer Diagnosis

References