COMPARATIVE STUDY OF CANCER PATIENT BY USING DATAMINING TECHNIQUES

Sri Vasavi College, Erode Self-Finance Wing 3rd February 2017 National Conference on Computer and Communication NCCC’17

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

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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. 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.

References

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Keywords

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

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

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