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Reseach Article

An Application of Classification Techniques on Breast Cancer Prognosis

by Sandeep Chaurasia, Prasun Chakrabarti, Neha Chourasia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 3
Year of Publication: 2012
Authors: Sandeep Chaurasia, Prasun Chakrabarti, Neha Chourasia
10.5120/9526-3946

Sandeep Chaurasia, Prasun Chakrabarti, Neha Chourasia . An Application of Classification Techniques on Breast Cancer Prognosis. International Journal of Computer Applications. 59, 3 ( December 2012), 6-10. DOI=10.5120/9526-3946

@article{ 10.5120/9526-3946,
author = { Sandeep Chaurasia, Prasun Chakrabarti, Neha Chourasia },
title = { An Application of Classification Techniques on Breast Cancer Prognosis },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 3 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number3/9526-3946/ },
doi = { 10.5120/9526-3946 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:07.385303+05:30
%A Sandeep Chaurasia
%A Prasun Chakrabarti
%A Neha Chourasia
%T An Application of Classification Techniques on Breast Cancer Prognosis
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 3
%P 6-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is the most frequently diagnosed cancer in USA; furthermore breast cancer is the second most frequent cause of death for women in the United States as well as in Asia. In USA 40,600 deaths from breast cancer in 2009, 400 were men. [1] Several well established tools are currently used to screen for breast cancer including clinical breast exams, mammograms, and ultrasound. Supervised training is a technique in which a set of representative input output pairs is presented to the network. Through an iterative algorithm, the interval network weights are adjusted to decrease the difference between the network prediction and the true result for the training cases. The test has been performed on the breast cancer dataset using three classification techniques: Bayes learner, Decision Tree and Neural Net. The experiment concludes that Neural Net performance is better than the Decision Tree classification and Naïve Bayes classification for early detection of breast cancer with better accuracy and precision.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Breast cancer Mammography Supervised Learning Neural Network Naïve Bayes Decision Tree