A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis
In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning. The paper presents a Support Vector Machine (SVM) approach for the prognosis and diagnosis of breast cancer implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and the Wisconsin Prognostic Breast Cancer (WPBC) datasets found in literature. The SVM algorithm performs excellently in both problems for the case study datasets, exhibiting high accuracy, sensitivity and specificity indices.
- 4.Street W. N., “A neural network model for prognostic prediction”, Proceedings of theFifteenth International Conference on Machine Learning, Madison, Wisconsin, Morgan Kaufmann, 1998.Google Scholar
- 5.Burges C: A tutorial on support vector machines for pattern recognition [http://www.kernel-machines.org/].Google Scholar
- 6.Schölkopf B.: Statistical learning and kernel methods [http://research.Microsoft.com/~bsc].Google Scholar
- 7.Campbell C.: Kernel methods: a survey of current techniques, [http://www.kernel-machines.org/].Google Scholar
- 13.Wolberg W.H., Street W.N., and Mangasarian O.L., Image analysis and machine learning applied to breast cancer diagnosis and prognosis, Analytical and Quantitative Cytology and Histology, Vol. 17, No. 2, pages 77–87, April 1995.Google Scholar
- 15.Kaban A., Girolami M., Initialized and guided EM-clustering of sparse binary data with application to text based documents, 15th International Conference on Pattern Recognition, Vol.2 pp.744–747, Sept. 2000.Google Scholar