Analysis of Machine Learning Techniques for Breast Cancer Prediction:

International Journal of Engineering and Management Research

View Publication Info
 
 
Field Value
 
Title Analysis of Machine Learning Techniques for Breast Cancer Prediction:
 
Creator Mrs N. Vanitha
R. Srimathi
J Haritha
 
Subject Breast Cancer
Prediction
Machine Learning
 
Description The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women.  With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.
 
Publisher Vandana Publications
 
Date 2021-02-09
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://www.ijemr.net/ojs/index.php/ojs/article/view/681
10.31033/ijemr.11.1.12
 
Source International Journal of Engineering and Management Research; Vol. 11 No. 1 (2021): February Issue; 79-83
2250-0758
2394-6962
 
Language eng
 
Relation https://www.ijemr.net/ojs/index.php/ojs/article/view/681/801
 
Rights Copyright (c) 2021 International Journal of Engineering and Management Research
https://creativecommons.org/licenses/by-nc-nd/4.0
 

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