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Chemotherapy Prediction of Cancer Patient by using Data Mining Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 76 - Number 10 |
| Year of Publication: 2013 |
| Authors: Reeti Yadav, Zubair Khan, Hina Saxena |
10.5120/13285-0747
|
Reeti Yadav, Zubair Khan, Hina Saxena . Chemotherapy Prediction of Cancer Patient by using Data Mining Techniques. International Journal of Computer Applications. 76, 10 ( August 2013), 28-31. DOI=10.5120/13285-0747
Abstract
Breast cancer is one of the prominent diseases for women in developed countries including India. It is the second most frequent cause of death in women. The identification of breast cancer patients for whom chemotherapy could prolong survival time is considered here as a data mining problem. We prescribe a procedure that uses support vector machines (SVMs) and Decision tree for classifying 100 breast cancer patients into two classes which are the two types of breast cancer diseases. It then compares the performance of both the classification techniques to find the better technique among them and use the appropriate technique for the next stage i. e. clustering. The identification is achieved by making clusters of above two classes into three prognostic groups: Good, Intermediate and Poor with the help of K-Means clustering technique. The result suggests that the patients in the Good group do not require chemotherapy. Chemotherapy is not of much importance in an Intermediate class while the Poor group is the most crucial group where chemotherapy can possibly enhance their survival.
References
- Breast Cancer statistics from Centers for Disease Control and Prevention,
- http://www. cdc. gov/cancer/breast/statistics/.
- D. M. Parkin, F. Bray, J. Ferlay, "Global cancer statistics 2002," CA Cancer J Clin, vol. 55, pp. 74-108, 2005.
- http://www. breastcancerindia. net/bc/statistics/stat_global. htm.
- Goharian & Grossman, (2003) "Data Mining Classification", Illinois Institute of Technology,http://ir. iit. edu/~nazli/cs422/CS422-Slides/DM-Classification. pdf.
- Lundin M, Lundin J, Burke HB, Toikkanen S, Pylkkanen L, Joensuu H. Artificial neural networks applied to survival prediction in breast cancer. Oncology 1999; 57:281—6.
- Abdelghani Bellaachia, Erhanguven, Predicting Breast cancer survivability using Data Mining Techniques.
- Abdelaal Ahmed Mohamed Medhat and Farouq Wael Muhamed, "Using data mining for assessing diagnosis of breast cancer," in Proc. International multiconference on computer science and information Technology, 2010, pp. 11-17.
- Maria-Luiza Antonie, Osmar R. Za¨?ane, Alexandru Coma, . Application of Data Mining Techniques for Medical Image Classification. Proceeding of second International workshop on Multimedia data mining (MDM/KDD'2001), in conjunction with ACM SIGKDD conference. SAN FRANCISCO, USA, AUG 26, 2001.
- Orlando Anunciac¸ ˜ao and Bruno C. Gomes and Susana Vinga and Jorge Gaspar and Arlindo L. Oliveira and Jos´e Rueff , A Data Mining Approach for the detection of High-Risk Breast Cancer Groups.
- V. Vapnik, (1998), Statistical Learning Theory, Wiley.
- V. Vapnik, (1998). "The support vector method of function estimation".
- X. Rui, "Survey of Clustering Algorithms", IEEE Transactions on Neural Networks, 16 (3), pp. 634-678, 2005.
- N. Chistianini & J. Shawe-Taylor, (2000). "An Introduction to Support Vector Machines, and other kernel-based learning methods", Cambridge University Press, 2000.
- N. Cristianini, & Shawe-Taylor, J. (2000) "An Introduction to Support Vector Machines". Cambridge University Press.
- L. Kaufman and P. J. Rousseeuw, "Finding Groups in Data: An Introduction to Cluster Analysis", John Wiley and Sons, March 1990.
- Delen, G. Walker, A. Kadam, "Predicting breast cancer survivability: comparison of three data mining methods," Artificial Intelligence in Medicine, vol. 34, pp. 113-127, 2005.
- D. Delen, G. Walker, A. Kadam, "Predicting breast cancer survivability: comparison of three data mining methods," Artificial Intelligence in Medicine, vol. 34, pp. 113-127, 2005.
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