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Breast Cancer Diagnosis Using Data Mining Classification Techniques Using Weka

Vandana Bharadi, Nidhi Mishra

Abstract


Breast cancer is currently posing a serious threat and is the second leading cause of death in women. A good and accurate diagnosis is important in order to control the very high recurrence rate of breast cancer. In this work, we explore the applicability of various data mining classifier to predict the presence of breast cancer. The performance of conventional supervised learning algorithms such as NaïveBayes, J4.8, SMO, IBk and MLP has been analyzed in this paper. Experipmental results are hereby proving that SMO to be the best one with highest accuracy and specificity, an average minimal error rate.

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Faraj A. El-Mouadib, Zakaria S. Zubi, Ahmed A. Alhouni , “New Implementation of Unsupervised ID3 Algorithm (NIU-ID3) Using Visual Basic.net”, 2007.

Glenn Fung, “A Comprehensive Overview of Basic Clustering Algorithms”, 2003.

Wei Peng, Juhua Chen, Haiping Zhou, “An Implementation of ID3 --- Decision Tree Learning Algorithm ”, Machine Learning, University of New South Wales, Australia, 2005.

Margaret H. Dunham, “Data Mining. Introductory and Advanced Topic”

Cluster Analysis: Basic Concepts and Algorithms

Liu Yuxun, Xie Niuniu, “Improved ID3 Algorithm”, IEEE Journals, 2010, Volume 1,106-T1364.

Devroye L, Gyorfi L, Lugosi G (1996) A probabilistic theory of pattern recognition. Springer, New York. ISBN 0-387-94618-7

J. Han and M. Kamber,”Data Mining Concepts and Techniques”, Morgan Kauffman Publishers, 2000.

Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. Proceedings IS&T/ SPIE International Symposium on Electronic Imaging 1993; 1905:861–70.

William H. Wolberg, M.D., W. Nick Street, Ph.D., Dennis M. Heisey, Ph.D., Olvi L. Mangasarian, Ph.D. computerized breast cancer diagnosis and prognosis from fine needle aspirates, Western Surgical Association meeting in Palm Desert, California, November 14, 1994.

Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.


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