Kernel-based naive bayes classifier for breast cancer prediction Nahar, Jesmin, Chen, Yi-Ping Phoebe and Ali, Shawkat 2007, Kernel-based naive bayes classifier for breast cancer prediction, Journal of biological systems, vol. 15, no. 1, pp. 17-25. Attached Files Name Description MIMEType Size Downloads Title Kernel-based naive bayes classifier for breast cancer prediction Author(s) Nahar, Jesmin Chen, Yi-Ping Phoebe Ali, Shawkat Journal name Journal of biological systems Volume number 15 Issue number 1 Start page 17 End page 25 Publisher World Scientific Publishing Company Place of publication Singapore Publication date 2007-03 ISSN 0218-3390 Keyword(s) Breast Cancer Prediction Classification Kernel-Based Naive Bayes Classifier Summary The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis. Language eng Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©World Scientific Publishing Company Persistent URL http://hdl.handle.net/10536/DRO/DU:30007570 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering and Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 11 Dec 2008, 08:40:11 EST Thu, 15 Sep 2011, 16:07:17 EST Thu, 15 Sep 2011, 16:08:16 EST Filtered Full Citation counts:   Cited 16 times in TR Web of Science Cited 19 times in Scopus Search Google Scholar Access Statistics: 914 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 29 Sep 2008, 08:53:38 EST

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