Application of Principal Component Analysis in the Diagnostic Classification of Breast Cancer

DOI

10.2991/978-94-6463-300-9_72How to use a DOI?

Keywords

Machine Learning; PCA; Breast Cancer

Abstract

In recent years, there has been a steady increase in the incidence of breast cancer, positioning it as the leading form of malignant tumors among women. Consequently, leveraging artificial intelligence (AI) technology to accurately classify and diagnose breast cancer has emerged as a crucial field of study within machine learning. This investigation demonstrates the implementation of the Principal Component Analysis (PCA) technique in the diagnostic classification of breast cancer, offering novel perspectives and methodologies for the development of breast cancer classifier models. The experimental design incorporated a control group, wherein the original breast cancer diagnosis dataset was subjected to dimensionality reduction using the PCA method. Subsequently, random forest classification and diagnosis were employed, and the resultant accuracies were compared across different groups. The experimental outcomes indicate that a decrease in the dimensionality achieved through the PCA method correspondingly leads to a decline in classification diagnosis accuracy. Overall, the accuracy of classification diagnosis post-PCA dimension reduction is suboptimal, considerably inferior to the control group utilizing random forest classification. This disparity can be attributed to the excessive original data dimensionality within this experiment, resulting in substantial information loss during PCA dimension reduction. Therefore, considerable potential exists for enhancing the utilization of the PCA preprocessing method in this domain, necessitating further improvements.

Copyright

© 2023 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  - Kunye Luo
PY  - 2023
DA  - 2023/11/27
TI  - Application of Principal Component Analysis in the Diagnostic Classification of Breast Cancer
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 687
EP  - 692
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_72
DO  - 10.2991/978-94-6463-300-9_72
ID  - Luo2023
ER  -