Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 7, Pages 9014-9022Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.01.120
Keywords
Breast cancer diagnosis; Rough set theory; Support vector machines; Feature selection
Categories
Funding
- National Natural Science Foundation of China (NSFC) [60603030, 60873149, 60973088, 60773099, 60703022]
- National High-Tech Research and Development Plan of China [2006AA10Z245, 2006AA10A309]
- Shanghai Key Laboratory of Intelligent Information Processing in Fudan University [IIPL-09-007]
- Erasmus Mundus External Cooperation Window's Project (EMECW): Bridging the Gap [155776-EM-1-2009-1-IT-ERAMUNDUS-ECW-L12]
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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. (C) 2011 Elsevier Ltd. All rights reserved.
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