A knowledge-based system for breast cancer classification using fuzzy logic method. Issue 4 (July 2017)

• Record Type: Journal Article
• Authors: Nilashi, Mehrbakhsh; Ibrahim, Othman; Ahmadi, Hossein; Shahmoradi, Leila
• Is Part Of: Telematics and informatics. Volume 34:Issue 4(2017)
• Page Start: 133
• Page End: 144
• Publication Date: 2017-07
• Subjects: Breast cancer -- Wisconsin Diagnostic Breast Cancer -- Mammographic mass -- Classification -- Fuzzy logic -- CART; Telecommunication -- Periodicals; Computer networks -- Periodicals; Télécommunications -- Périodiques; Réseaux d'ordinateurs -- Périodiques; 384
• DOI: 10.1016/j.tele.2017.01.007
• Languages: English
• ISSNs: 0736-5853

Abstract

Highlights: A knowledge-based system is proposed for breast cancer disease classification. The knowledge-based system uses EM, PCA, CART and fuzzy rule-based methods. WDBC and Mammographic mass datasets are used for the method evaluation. The accuracies obtained by the method are respectively 0.932 and 0.941 for WDBC and Mammographic mass datasets. Abstract: Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.