Application of Machine Learning in Prediction of Breast Cancer

DOI

10.2991/978-94-6463-540-9_9How to use a DOI?

Keywords

Machine learning; breast cancer; deep learning

Abstract

Breast cancer is a malignant tumor that develops from the cells of breast tissue, typically from the ducts or glands. Artificial Intelligence (AI) technology has become a viable option for the automated detection of breast cancer. The study aims to provide a comprehensive review about the application of various machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in predicting breast cancer risk. The discussion showed that deep learning neural networks outperformed other models, demonstrating promising potential in clinical applications. The findings suggest that deep learning neural networks offer superior accuracy in breast cancer prediction, highlighting their significance in healthcare advancements. Key challenges such as model interpretability, data distribution differences, and privacy are addressed, emphasizing the need for transparent models and secure data handling techniques like federated learning in the future studies. This paper can be considered as an effective reference for this field.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chuanqi Yu
PY  - 2024
DA  - 2024/10/16
TI  - Application of Machine Learning in Prediction of Breast Cancer
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 70
EP  - 75
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_9
DO  - 10.2991/978-94-6463-540-9_9
ID  - Yu2024
ER  -