A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis

Authors:
Hui-Ling Chen;Bo Yang;Jie Liu;Da-You Liu
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin Universit ...;College of Computer Science and Technology, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin Universit ...;College of Computer Science and Technology, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin Universit ...;College of Computer Science and Technology, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin Universit ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. Expert systems and machine learning techniques are gaining popularity in this field because of the effective classification and high diagnostic capability. In this paper, a rough set (RS) based supporting vector machine classifier (RS_SVM) is proposed for breast cancer diagnosis. In the proposed method (RS_SVM), RS reduction algorithm is employed as a feature selection tool to remove the redundant features and further improve the diagnostic accuracy by SVM. The effectiveness of the RS_SVM is examined on Wisconsin Breast Cancer Dataset (WBCD) using classification accuracy, sensitivity, specificity, confusion matrix and receiver operating characteristic (ROC) curves. Experimental results demonstrate the proposed RS_SVM can not only achieve very high classification accuracy but also detect a combination of five informative features, which can give an important clue to the physicians for breast diagnosis.