Breast Cancer Diagnosis with Machine Learning
DOI:
https://doi.org/10.54097/hset.v9i.1718Keywords:
Breast cancer classification; Logistic Regression; Machine Learning.Abstract
An early-stage breast cancer diagnosis usually results in a high survival rate within five years. This research seeks the feasibility of applying a specific machine learning algorithm, logistic regression, to a public dataset. The result is two models, reaching accuracies of 0.965 and 0.936. It confirms that the generality of applying machine learning algorithms is beneficial to medical diagnosis. Even though this is not the first research to exploit the application of machine learning in the medical field, it still provides some valuable insights for future research.
Downloads
References
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Attaran, M., & Deb, P. (2018). Machine learning: the new “big thing” for competitive advantage. International Journal of Knowledge Engineering and Data Mining, 5(4), 277. https://doi.org/10.1504/ijkedm.2018.095523
Ahsan, M. M., & Siddique, Z. (2022). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128, 102289. https://doi.org/10.1016/j.artmed.2022.102289
Osareh, A., & Shadgar, B. (2010). Machine learning techniques to diagnose breast cancer. 2010 5th International Symposium on Health Informatics and Bioinformatics. https://doi.org/10.1109/hibit.2010.5478895
Amrane, M., Oukid, S., Gagaoua, I., & Ensari, T. (2018). Breast cancer classification using machine learning. 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT). https://doi.org/10.1109/ebbt.2018.8391453
Stimpfel, M., & Virant-Klun, I. (2016). Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Journal of Cancer Stem Cell Research, 4(3), 1. https://doi.org/10.14343/jcscr.2016.4e1003
Sinha, T. (2018). Tumors: Benign and Malignant. Cancer Therapy & Oncology International Journal, 10(3). https://doi.org/10.19080/ctoij.2018.10.555790
Zeng, H., Zheng, R., Guo, Y., Zhang, S., Zou, X., Wang, N., Zhang, L., Tang, J., Chen, J., Wei, K., Huang, S., Wang, J., Yu, L., Zhao, D., Song, G., Chen, J., Shen, Y., Yang, X., Gu, X., . . . Yu, X. Q. (2014, October 3). Cancer survival in China, 2003-2005: A population-based study. International Journal of Cancer, 136(8), 1921–1930. https://doi.org/10.1002/ijc.29227
Cancer Facts & Figures 2010 | American Cancer Society. (n.d.). Retrieved September 7, 2022, from https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2010.html
Bennett, Kristin P. (1992). Decision Tree Construction Via Linear Programming (No. TR1067). The University of Wisconsin-Madison. https://minds.wisconsin.edu/handle/1793/59564
Zou, X., Hu, Y., Tian, Z., & Shen, K. (2019, October). Logistic Regression Model Optimization and Case Analysis. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). https://doi.org/10.1109/iccsnt47585.2019.8962457
Zeeshan, M., Salam, B., Khalid, Q. S. B., Alam, S., & Sayani, R. (2018, April 8). Diagnostic Accuracy of Digital Mammography in the Detection of Breast Cancer. Cureus. https://doi.org/10.7759/cureus.2448
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







