International Journal of Bioinformatics and Biomedical Engineering
Articles Information
International Journal of Bioinformatics and Biomedical Engineering, Vol.1, No.3, Nov. 2015, Pub. Date: Dec. 30, 2015
Diagnosis of Breast Cancer Using Ensemble of Data Mining Classification Methods
Pages: 318-322 Views: 694 Downloads: 1082
Authors
[01] Gokhan Zorluoglu, Bioengineering Department, Marmara University, Kadikoy, Istanbul, Turkey.
[02] Mustafa Agaoglu, Computer Engineering Department, Marmara University, Kadikoy, Istanbul, Turkey.
Abstract
Breast cancer is a very serious malignant tumor originating from the breast cells. The disease occurs generally in women, but also men can rarely have it. During the prognosis of breast cancer, abnormal growth of cells in breast takes place and this growth can be in two types which are benign (non-cancerous) and malignant (cancerous). In this study, the aim is to diagnose the breast cancer using various intelligent techniques including Decision Trees (DT), Support Vector Machines (SVM), Artificial Neural Network (ANN) and also the ensemble of these techniques. Experimental studies were done using SPSS Clementine software and the results show that the ensemble model is better than the individual models according to the evaluation metric which is the accuracy. In order to increase the efficiency of the models, feature selection technique is applied. Moreover, models are also analyzed in terms of other error measures like sensitivity and specificity.
Keywords
Artificial Neural Network, Breast Cancer, Cross Validation, C5.0, Data Mining, Decision Tree, Support Vector Machine
References
[01] Makinacı, M., Güneşer, C. (no date). Göğüs Kanseri Verilerinin Sınıflandırılması.
[02] Mangasarian, O. L., Street, W. N., Wolberg, W. H. (1995). Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pp. 570-577.
[03] Kharya, S. (2012). Using Data Mining Techniques for Diagnosis And Prognosis of Cancer Disease, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 2, No. 2。
[04] Wolberg, W. H., Street, W. N, Mangasarian, O. L. (1994). Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates. Cancer Letters vol. 77, 163-171.
[05] Wolberg, W. H., Street, W. N., Mangasarian, O. L. (1995a). Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pp. 77-87.
[06] Wolberg, W. H., Street, W. N., Heisey, D. M., Mangasarian, O. L. (1995b). Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Archives of Surgery; 130:511-516.
[07] Wolberg, W. H., Street, W. N., Heisey, D. M., Mangasarian, O. L. (1995c) Computer-derived nuclear features distinguish malignant from benign breast cytology. Human Pathology, 26: 792-796.
[08] Bache, K., Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
[09] Bennett, K. P. (1992). Decision tree construction via linear programming. In Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society Conference, pp. 97-101.
[10] Bennett, K. P., Mangasarian, O. L (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1: 23-34.
[11] Majali, J., Niranjan, R., Phatak, V. and Tadakhe, O. (2014). Data Mining Techniques For Diagnosis And Prognosis of Breast Cancer. International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (5), 2014, 6487-6490.
[12] Sumbaly, R., N. Vishnusri, S. Jeyalatha (2014). Diagnosis of Breast Cancer using Decision Tree Data Mining Technique. International Journal of Computer Applications (0975 – 8887) Volume 98 – No. 10.
[13] Bhargava, N., Sharma, G., Bhargava, R. and Mathuria, M. (2013). Decision Tree Analysis on J48 Algorithm for Data Mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6.
[14] H. Blockeel and J. Struyf (2001). Efficient algorithms for decision tree cross-validation. Proceedings of the Eighteenth International Conference on Machine Learning (C. Brodley and A. Danyluk, eds.), Morgan Kaufmann, pp. 11-18.
[15] Feld, M., Kipp, M., Ndiaye, A. and Heckmann, D. (no date). Weka: Practical machine learning tools and techniques with Java implementations.
[16] Gupta, S., Kumar, D. and Sharma, A. (2011). Data Mining Classification Techniques Applied for Breast Cancer Diagnosis and Prognosis. Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2 No. 2.
[17] Nicholas, E. (2008). Introduction to Clementine and Data Mining. Brigham Young University.
[18] Salama, G. I., Abdelhalim, M. B., Zeid, M. A. (2012). Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers. International Journal of Computer and Information Technology (2277 – 0764), Volume 01– Issue 01.
[19] 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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