The week's pick
Reseach Article
Predicting Breast Cancer Recurrence using Data Mining Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 122 - Number 23 |
| Year of Publication: 2015 |
| Authors: Siddhant Kulkarni, Mangesh Bhagwat |
10.5120/21866-5196
|
Siddhant Kulkarni, Mangesh Bhagwat . Predicting Breast Cancer Recurrence using Data Mining Techniques. International Journal of Computer Applications. 122, 23 ( July 2015), 26-31. DOI=10.5120/21866-5196
Abstract
Breast Cancer is among the leading causes of cancer death in women. In recent times, the occurrence of breast cancer has increased significantly and a lot of organizations are taking up the cause of spreading awareness about breast cancer. With early detection and treatment it is possible that this type of cancer will go into remission. In such a case, the worse fear of a cancer patient is the recurrence of the cancer. This paper evaluates various data mining techniques and their ability to predict whether any particular patient will face a recurrence. Experimental results will show the accuracy of various classifiers when applied on the Breast Cancer Dataset[1].
References
- Marios Skevofilakas, Konstantina Nikita, Panagiotis Templaleksis, K. Birbas, I. Kaklamanos, G. Bonatsos, "A decision support system for breast cancer treatment based on data mining technologies and clinical practice guidelines", pp. 2429 - 2432, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, China, Sept. 2005.
- Menolascina F, Tommasi S, Paradiso A, Cortellino M, Bevilacqua V, Mastronardi G, "Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective", pp. 9-16, Proceedings of the 2007 IEEE symposium on computational Intelligence in Bioinformatics and Computational Biology, 2007.
- Chi-Shih Yang, Ming-Yih Lee, "Parametric Data Mining and Diagnosis Rules for Digital Thermographs in Breast Cancer", pp. 98-101, 30th Annual International IEEE Conference Vancover, Canada, August 2008.
- A. Soltani Sarvestani, A. A. Safavi, N. M. Parandeh, M. Salehi, "Predicting Breast Cancer Survivability using data mining techniques", pp. V2-227 - V2-231, 2nd International Conference on Software Technology and Engineering, 2010.
- Gouda Salama, M. B. Abdelhalim, Magdy Zeid, "Experimental Comparison of Classifiers for Breast Cancer Diagnosis", Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on , vol. , no. , pp. 180,185, 27-29 Nov. 2012.
Index Terms
Keywords