A Critical Study of Significant Classification Techniques for Diagnosis of Breast Cancer
Breast cancer is one of the leading cancers for women in developed countries including India. The classification of breast cancer patients is one of the challenging research problems. This paper comes with the selected classification algorithms for the classification of breast cancer patient datasets. The implementation is done using the application of Bayes Net, Naïve Bayes classifier, C4.5, Back propagation, and Support Vector Machines. The performance of the algorithm is evaluated using classification accuracy, sensitivity, specificity, and precision values. The experiments are done using 10 fold cross validation method. The results obtained by Bayes Net are superior by other classifiers
Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, second edition, Morgan Kaufmann Publishers an imprint of Elsevier.UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets.html]. Irvine, CA:University of California, School of Information and Computer Science.
WEKA, by university of Waikato, http://www.cs.waikato.ac.nz/ml/weka/
Paul R. Harper, A review and comparison of classification algorithms for medical decision making.
G. H. John and P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers,” Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, San Francisco, 1995, pp. 338-345.
Bendi Venkata Ramana, Prof. M.Surendra Prasad Babu and Prof. N. B. Venkateswarlu:” A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis”. In Proceedings of the International Journal of Database Management Systems ( IJDMS ), Vol.3, No.2, pages 101- 114, May 2011.
Michael J. Sorich,† John O. Miners,*,‡ Ross A. McKinnon,† David A. Winkler,§ Frank R. Burden, and Paul A. Smith‡ Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP- Glucuronosyltransferase Isoforms.
Paul R. Harper, A review and comparison of classification algorithms for decision making.
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