Book Details


Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Download this PDF format


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. 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. We have investigated more of data mining techniques: the Naïve Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms. Several experiments were conducted using these algorithms. The achieved prediction performances are comparable to existing techniques. However, we found out that C4.5 algorithm has a much better performance than the other techniques.


[1] Breast Cancer statistics from Centers for Disease Control and Prevention,


[3] D. M. Parkin, F. Bray, J. Ferlay, ?Global cancer statistics 2002,? CA Cancer J Clin, vol.55, pp. 74-108, 2005.

[4] .htm.

[5] Goharian & Grossman, (2003) ?Data Mining Classification?, Illinois Institute of Technology, Slides/DM-Classification.pdf.

[6] 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.

[7] Abdelghani Bellaachia, Erhanguven, Predicting Breast cancer survivability using Data Mining Techniques.

[8] 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.

[9] Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine. 2005 Jun; 34(2):113-27.

[10] Ian H. Witten and Eibe Frank. Data Mining: Practical machine learning tools and techniques, 2nd Edition. San Fransisco:Morgan Kaufmann; 2005 .

[11] J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann; 1993.

[12] Weka: Data Mining Software in Java,


Clustering, SVM, decision tree, k-means, classification, data mining, Breast cancer survivability, data mining, SEER, Weka.

  • Format Volume 5, Issue 1, No 14, 2017
  • Copyright All Rights Reserved ©2017
  • Year of Publication 2017
  • Author M.karthika, S.Muruganandam
  • Reference IJCS-221
  • Page No 1379-1385

Copyright 2022 SK Research Group of Companies. All Rights Reserved.