Predicting Breast Cancer Recurrence using Data Mining Techniques
作者： Siddhant Kulkarni , Mangesh Bhagwat
DOI: 10.5120/21866-5196
关键词:
摘要: Breast Cancer is among the leading causes of cancer death in women. In recent times, occurrence breast has increased significantly and a lot organizations are taking up cause spreading awareness about cancer. With early detection treatment it possible that this type will go into remission. such case, worse fear patient recurrence This paper evaluates various data mining techniques their ability to predict whether any particular face recurrence. Experimental results show accuracy classifiers when applied on Dataset[1].
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Gouda I. Salama, M. B. Abdelhalim, Magdy Abd-elghany Zeid, Experimental comparison of classifiers for breast cancer diagnosis international conference on computer engineering and systems. pp. 180- 185 ,(2012) , 10.1109/ICCES.2012.6408508
A Soltani Sarvestani, AA Safavi, NM Parandeh, M Salehi, None, Predicting breast cancer survivability using data mining techniques international conference on software technology and engineering. ,vol. 2, ,(2010) , 10.1109/ICSTE.2010.5608818
Marios T. Skevofilakas, Konstatina S. Nikita, Panagiotis H. Templaleksis, K.N. Birbas, I.G. Kaklamanos, G.N. Bonatsos, A decision support system for breast cancer treatment based on data mining technologies and clinical practice guidelines international conference of the ieee engineering in medicine and biology society. ,vol. 2005, pp. 2429- 2432 ,(2005) , 10.1109/IEMBS.2005.1616959
Chi-Shih Yang, Ming-Yih Lee, Parametric data mining and diagnostic rules for digital thermographs in breast cancer international conference of the ieee engineering in medicine and biology society. ,vol. 2008, pp. 98- 101 ,(2008) , 10.1109/IEMBS.2008.4649100
F. Menolascina, S. Tommasi, A. Paradiso, M. Cortellino, V. Bevilacqua, G. Mastronardi, Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective computational intelligence in bioinformatics and computational biology. pp. 9- 16 ,(2007) , 10.1109/CIBCB.2007.4221198