Breast cancer classification based on hybrid machine learning model

Research Article
Open access

Breast cancer classification based on hybrid machine learning model

Zhe Lin 1*
  • 1 Shanghai University    
  • *corresponding author 3090534795@shu.edu.cn
Published on 26 February 2024 | https://doi.org/10.54254/2755-2721/43/20230813
ACE Vol.43
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-311-1
ISBN (Online): 978-1-83558-312-8

Abstract

This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.

Keywords:

Breast Cancer, K-Means Algorithm, Random Forest

Lin,Z. (2024). Breast cancer classification based on hybrid machine learning model. Applied and Computational Engineering,43,94-98.
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References

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[2]. Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., and Madabhushi, A.: Accurate and reproducible invasive breast cancer detection in whole-slide images: A deep learning approach for quantifying tumor extent. Scientific Reports, 3, 1564 (2013).

[3]. Huang, P. S., Wei, C. Y., Chen, C. H., Wu, T. T., Chen, Y. Y., & Chen, C. C. An intelligent breast cancer detection system using support vector machines. Journal of Medical Systems, 35(5), 1165-1174 (2011).

[4]. Cheng, J. Z., Ni, D., Chou, Y. H., Qin, J., Tiu, C. M., Chang, Y. C., ... & Wang, X. Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific Reports, p 6, 24454 (2016).

[5]. Kazerooni, F., Lotfollahi, M., et al.: Deep neural network-based classification of breast cancer histopathological images using transfer learning. Computer Methods and Programs in Biomedicine, p 208, 106252 (2021).

[6]. The National Health Service, https://www.nhs.uk/ (2023).

[7]. IBM Watson for Oncology, https://www.ibm.com/watson/health/oncology-and-genomics/oncology/ (2023).

[8]. Breast cancer gene expression - CuMiDa | Kaggle,[EB/OL], https://www.kaggle. com/datasets/brunogrisci/breast-cancer-gene-expression-cumida (2020).

[9]. Feltes, B.C., Chandelier, E. B., Grisci, B. I., Dorn, M.: CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. Journal of Computational Biology, p 26 (4), pp 376-386 (2019).

[10]. Lloyd, S. P.: Least squares quantization in PCM. IEEE Transactions on Information Theory, p 28(2), pp 129-137 (1982).

[11]. MacQueen, J.: Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, p 1(14), pp 281-297 (1967).

[12]. Breiman, L.: Random forests. Machine Learning, p 45(1), pp 5-32 (2001).

[13]. Yu, Q., Chen, P., Lin, Z., et al.: Clustering Analysis for Silent Telecom Customers Based on K-means++, 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2020, 1: 1023-1027 (2023).

[14]. Scikit-learn; Machine Learning in Python [EB/OL], https://scikit-learn.org/ (2023).

Cite this article

Lin,Z. (2024). Breast cancer classification based on hybrid machine learning model. Applied and Computational Engineering,43,94-98.

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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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-311-1(Print) / 978-1-83558-312-8(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.43
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118 (2017).

[2]. Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., and Madabhushi, A.: Accurate and reproducible invasive breast cancer detection in whole-slide images: A deep learning approach for quantifying tumor extent. Scientific Reports, 3, 1564 (2013).

[3]. Huang, P. S., Wei, C. Y., Chen, C. H., Wu, T. T., Chen, Y. Y., & Chen, C. C. An intelligent breast cancer detection system using support vector machines. Journal of Medical Systems, 35(5), 1165-1174 (2011).

[4]. Cheng, J. Z., Ni, D., Chou, Y. H., Qin, J., Tiu, C. M., Chang, Y. C., ... & Wang, X. Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific Reports, p 6, 24454 (2016).

[5]. Kazerooni, F., Lotfollahi, M., et al.: Deep neural network-based classification of breast cancer histopathological images using transfer learning. Computer Methods and Programs in Biomedicine, p 208, 106252 (2021).

[6]. The National Health Service, https://www.nhs.uk/ (2023).

[7]. IBM Watson for Oncology, https://www.ibm.com/watson/health/oncology-and-genomics/oncology/ (2023).

[8]. Breast cancer gene expression - CuMiDa | Kaggle,[EB/OL], https://www.kaggle. com/datasets/brunogrisci/breast-cancer-gene-expression-cumida (2020).

[9]. Feltes, B.C., Chandelier, E. B., Grisci, B. I., Dorn, M.: CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. Journal of Computational Biology, p 26 (4), pp 376-386 (2019).

[10]. Lloyd, S. P.: Least squares quantization in PCM. IEEE Transactions on Information Theory, p 28(2), pp 129-137 (1982).

[11]. MacQueen, J.: Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, p 1(14), pp 281-297 (1967).

[12]. Breiman, L.: Random forests. Machine Learning, p 45(1), pp 5-32 (2001).

[13]. Yu, Q., Chen, P., Lin, Z., et al.: Clustering Analysis for Silent Telecom Customers Based on K-means++, 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2020, 1: 1023-1027 (2023).

[14]. Scikit-learn; Machine Learning in Python [EB/OL], https://scikit-learn.org/ (2023).

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