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Reseach Article

An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis

Published on December 2013 by Mahua Nandy
2nd International conference on Computing Communication and Sensor Network 2013
Foundation of Computer Science USA
CCSN2013 - Number 2
December 2013
Authors: Mahua Nandy
3200d40c-e0e1-4a75-9bde-36b712fbba8a

Mahua Nandy . An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis. 2nd International conference on Computing Communication and Sensor Network 2013. CCSN2013, 2 (December 2013), 1-4.

@article{
author = { Mahua Nandy },
title = { An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis },
journal = { 2nd International conference on Computing Communication and Sensor Network 2013 },
issue_date = { December 2013 },
volume = { CCSN2013 },
number = { 2 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ccsn2013/number2/14759-1317/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd International conference on Computing Communication and Sensor Network 2013
%A Mahua Nandy
%T An Analytical Study of Supervised and Unsupervised Classification Methods for Breast Cancer Diagnosis
%J 2nd International conference on Computing Communication and Sensor Network 2013
%@ 0975-8887
%V CCSN2013
%N 2
%P 1-4
%D 2013
%I International Journal of Computer Applications
Abstract

In this work, ANN and SVM, two most popular supervised machine learning techniques, are considered as the representatives and k-means clustering is used as representative of unsupervised learning. By analyzing the diagnosis result using Wisconsin Breast Cancer Dataset (WBCD) which is commonly used among researchers who use machine learning methods for breast cancer diagnosis, it can be concluded that SVM outperforms in case of breast cancer diagnosis. The result is verified using two other breast cancer datasets. One is Breast Cancer Dataset from UCI Machine Learning Repository and another one is "Breast cancer dataset with Electrical Impedance Measurements in samples of freshly excised tissue". The purpose of the comparison is to choose the best solution in terms of performance. Another notable significance of the work is that accuracy of the recognition drops down severely if proper feature set is not used. One significant disadvantage of neural network is its time taken to build the model which is also evident from the work.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Ann Svm K-means Clustering