Machine learning based breast cancer detection using logistic regression

Breast cancer is a disease that affects people all over the world and can be fatal. On the other hand, if identified early enough, this deadly disease will save many lives. Radiologists use mammography images to determine the presence or absence of breast cancer. Machine learning techniques are used...

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Bibliographic Details
Main Authors: Taraniya, I., P.V., Bhaskar Reddy, Divyasri, Yeddula, Chaithra, Varshini, Raviteja, Nalam Lakshmi
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Bioinformatics
Breast cancer
Disease
Machine learning
Mammography
Regression analysis
Supervised learning
Online Access:Get full text
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Summary:Breast cancer is a disease that affects people all over the world and can be fatal. On the other hand, if identified early enough, this deadly disease will save many lives. Radiologists use mammography images to determine the presence or absence of breast cancer. Machine learning techniques are used in bioinformatics, specifically for breast cancer diagnosis. This research tests the most widely used Supervised Machine Learning Algorithms with logistic regression and binary classification. In this analysis, the University of Wisconsin’s Breast Cancer Data Set (BCD) is used to predict Breast Cancer. This dataset takes a number of factors into account when diagnosing the presence or absence of breast cancer, as well as the stage of the disease. Fine needle aspiration (FNA) is a surgical technique that is used to locate the affected cells. Using the supervised machine learning algorithm, the proposed work achieved a best accuracy of 96.15 percent. As a result, our primary goal is to assist people in curing diseases at an early stage and allowing them to live peacefully.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0200498