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Study and Analysis of Breast Cancer Cell Detection using Naïve Bayes, SVM and Ensemble Algorithms

by Animesh Hazra, Subrata Kumar Mandal, Amit Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 2
Year of Publication: 2016
Authors: Animesh Hazra, Subrata Kumar Mandal, Amit Gupta
10.5120/ijca2016910595

Animesh Hazra, Subrata Kumar Mandal, Amit Gupta . Study and Analysis of Breast Cancer Cell Detection using Naïve Bayes, SVM and Ensemble Algorithms. International Journal of Computer Applications. 145, 2 ( Jul 2016), 39-45. DOI=10.5120/ijca2016910595

@article{ 10.5120/ijca2016910595,
author = { Animesh Hazra, Subrata Kumar Mandal, Amit Gupta },
title = { Study and Analysis of Breast Cancer Cell Detection using Naïve Bayes, SVM and Ensemble Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 2 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number2/25254-2016910595/ },
doi = { 10.5120/ijca2016910595 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:00.613318+05:30
%A Animesh Hazra
%A Subrata Kumar Mandal
%A Amit Gupta
%T Study and Analysis of Breast Cancer Cell Detection using Naïve Bayes, SVM and Ensemble Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 2
%P 39-45
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is one of the second leading causes of cancerdeath in women. Despite the fact that cancer is preventable and curable in primary stages, the huge number of patients are diagnosed with cancer very late. Conventional methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer [1]. The objective of this paper is to find the smallest subset of features that can ensure highly accurate classification of breast cancer as either benign or malignant. Then a comparative study on different cancer classification approaches viz. Naïve Bayes, Support Vector Machine and Ensemble classifiers is conducted where the time complexity of each of the classifier is also measured. Here, Naïve Bayes classifier is concluded as the best classifier with lowest time complexity as compared to the other two classifiers.

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Index Terms

Computer Science
Information Sciences

Keywords

Supervised machine learning benign cancer classification malignant.

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