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