Machine Learning Techniques for Breast Cancer Detection

Karl Hall, Victor Chang, Paul Mitchell

2022

Abstract

Breast cancer is the second most prevalent type of cancer overall and the most frequently occurring cancer in women. The most effective way to improve breast cancer survival rates still lies in the early detection of the disease. An increasingly popular and effective way of doing this is by using machine learning to classify and analyze patient data to help identify signs of cancer. This paper explores a variety of machine learning techniques and compares their prediction accuracy and other metrics when using the Breast Cancer Wisconsin (Original) data set using 10-fold cross-validation methods. Of the algorithms tested in this paper, a support vector machine model using the radial basis function kernel outperformed all other models we tested and those previously developed by others, achieving an accuracy of 99%.

Paper Citation

in Harvard Style

Hall K., Chang V. and Mitchell P. (2022). Machine Learning Techniques for Breast Cancer Detection. In Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS, ISBN 978-989-758-565-4, pages 116-122. DOI: 10.5220/0011123200003197

in Bibtex Style

@conference{complexis22,
author={Karl Hall and Victor Chang and Paul Mitchell},
title={Machine Learning Techniques for Breast Cancer Detection},
booktitle={Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,},
year={2022},
pages={116-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011123200003197},
isbn={978-989-758-565-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - Machine Learning Techniques for Breast Cancer Detection
SN - 978-989-758-565-4
AU - Hall K.
AU - Chang V.
AU - Mitchell P.
PY - 2022
SP - 116
EP - 122
DO - 10.5220/0011123200003197