Diagnosing Breast Cancer with Machine Learning

Haoran Wang

doi:10.54254/2753-8818/2025.AU28004

Theoretical and Natural ScienceOpen access

Diagnosing Breast Cancer with Machine Learning

Research Article

Open Access

Diagnosing Breast Cancer with Machine Learning

Haoran Wang ^1*

¹ Huazhong University of Science and Technology

^*Corresponding author: u202210201@hust.edu.cn

Published on 23 October 2025

TNS Vol.144

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-441-0

ISBN (Online): 978-1-80590-442-7

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Abstract

To develop a model that establishes if a patient's breast cancer is benign or malignant, it uses them in the patient data set. The issue discussed in this article is part of a larger problem that is classified as supervised learning. Python is employed for coding. This study makes use of Keras, which is linked to TensorFlow and serves as one of the most widely used free modules. It's a categorization problem that is binary, and neurons are employed to create the model. The sigmoid function is employed in the output layer as a means of activation. The article attempts to create different models with different numbers of hidden layers. A set for verification (25%) and the training data (75%), respectively, are created from the collected data. The validation set is used to assess each model's success after it has been first trained on the set that was used for training. The article's conclusion states that the model with no additional layer is the most effective.

Keywords:

Machine learning, Supervised learning, Binary classification problem, Logistic regression, Neural network, Breast cancer

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References

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References

[1]. Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https: //doi.org/10.1007/s12525-021-00459-5

[3]. Sharma, N., Sharma, R., & Jindal, N. (2021). Machine learning and deep learning applications-a vision. Global Transitions Proceedings, 2(1), 24-28. https: //doi.org/10.1016/j.glt.2021.09.004

[11]. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc.

[12]. Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168, 012013. https: //doi.org/10.1088/1742-6596/1168/1/012013

Cite this article

Wang,H. (2025). Diagnosing Breast Cancer with Machine Learning. Theoretical and Natural Science,144,20-26.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICBioMed 2025 Symposium: AI for Healthcare: Advanced Medical Data Analytics and Smart Rehabilitation

ISBN: 978-1-80590-441-0(Print) / 978-1-80590-442-7(Online)

Editor: Alan Wang

Conference website: https://2025.icbiomed.org/auckland.html

Conference date: 17 October 2025

Series: Theoretical and Natural Science

Volume number: Vol.144

ISSN: 2753-8818(Print) / 2753-8826(Online)

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