Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 17, 04 December 2023
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Breast cancer survival data prediction using machine learning model
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Abstract
Breast cancer is the most common form of cancer affecting women, exerting a significant impact on individuals, families, and societies globally. With its multifaceted nature, breast cancer research and awareness efforts have gained substantial momentum, leading to transformative breakthroughs in understanding its causes, diagnosis, treatment, and prevention. Survival analysis is a pivotal statistical tool in understanding the dynamic and often complex trajectory of breast cancer. As a disease that evolves, breast cancer research benefits immensely from survival analysis, which provides insights into patient outcomes, treatment efficacy, and the influence of various factors on survival. In this paper, Haberman’s Survival Dataset is used to analyze the data on breast cancer. The primary objective of this study is to establish the correlation between input and output variables, along with identifying significant features. The overarching aim of this research is to assess and compare the efficacy of various machine learning models in order to ascertain the optimal one.
Keywords
Breast Cancer, Prediction, Survival Analysis, Machine Learning Model
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-197-1
- ISBN (Online)
- 978-1-83558-198-8
- Published Date
- 04 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/17/20240652
- Copyright
- 04 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited