Machine Learning and Feature Selection for Breast Cancer Prediction

DOI

10.2991/978-2-38476-585-0_35How to use a DOI?

Keywords

Machine Learning; Breast Cancer Diagnosis; Feature Selection; WDBC Dataset

Abstract

One of the most prevalent and fatal tumors that impact women globally is breast cancer. Traditional diagnostic methods, while effective, can be costly. The goal of this research is to improve the precision and effectiveness of breast cancer detection by combining feature selection techniques with machine learning models. Seven machine learning models were trained and assessed using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset in conjunction with three feature selection strategies: filter method, univariate selection (SelectKBest), and embedded method (Random Forest importance). Experimental results show that neural networks achieved the highest performance when using all features, while ensemble models performed best when used with filter feature selection. The study found that the choice of feature selection method should be aligned with the nature of the model, and combining suitable selection strategies with machine learning models can significantly enhance diagnostic performance. This approach can reduce misdiagnosis and improve early treatment outcomes.

Copyright

© 2026 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  - Xinlei He
PY  - 2026
DA  - 2026/06/18
TI  - Machine Learning and Feature Selection for Breast Cancer Prediction
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 294
EP  - 301
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_35
DO  - 10.2991/978-2-38476-585-0_35
ID  - He2026
ER  -