Comparison of machine learning models on breast cancer risk prediction challenge

Research Article
Open access

Comparison of machine learning models on breast cancer risk prediction challenge

Mingjie Chen 1*
  • 1 Xi’ an Jiaotong - Liverpool University    
  • *corresponding author Mingjie.Chen2002@student.xjtlu.edu.cn
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230203
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

Breast cancer is a malignant tumor that poses a serious risk to women's life and wellbeing. To make matters worse, this cancer is less symptomatic in its early stages. It is not easily diagnosed through traditional means. The topic of this essay is to investigate machine learning for the determination of breast cancer.. The methods based on machine learning are as followed: automated nuclear section segmentation model, BCRecommender System, DNNs, and computer-aided diagnosis model (CADM). The methods studied are all based on the BreCaHad dataset and use a comparative metric.To measure the performance of each model, the accuracy, F1-score, specificity, and precision are used. The result shows that the approaches based on machine learning work well in diagnosing breast carcinoma, with high accuracy. Most of them have a percentage over 90% in accuracy and some of them are even higher than 95%. However, some of the models work poorly, such as layer 1 of BCRecommender with 61.06% accuracy and EfficientNetB0 with 72.96%. With every aspect taken into consideration, computer-aided diagnosis (CADM: Using combined features of HOG, WPD, ResNet as well as PCA + SVM) has the greatest advantage in diagnosing BC.

Keywords:

Breast cancer prediction, deep learning, machine learning

Chen,M. (2023). Comparison of machine learning models on breast cancer risk prediction challenge. Applied and Computational Engineering,27,179-184.
Export citation

References

[1]. Gabriel, C. A., & Domchek, S. M. (2010). Breast cancer in young women. Breast cancer research, 12, 1-10.

[2]. Nelson, H. D., Pappas, M., Cantor, A., Griffin, J., Daeges, M., & Humphrey, L. (2016). Harms of breast cancer screening: systematic review to update the 2009 US Preventive Services Task Force Recommendation. Annals of internal medicine, 164(4), 256-267.

[3]. Kerlikowske, K., Zhu, W., Tosteson, A. N., Sprague, B. L., Tice, J. A., et, al. (2015). Identifying women with dense breasts at high risk for interval cancer: a cohort study. Annals of internal medicine, 162(10), 673-681.

[4]. Tagliafico, A. S., Piana, M., Schenone, D., Lai, R., Massone, A. M., & Houssami, N. (2020). Overview of radiomics in breast cancer diagnosis and prognostication. The Breast, 49, 74-80.

[5]. Phi, X. A., Tagliafico, A., Houssami, N., Greuter, M. J., & de Bock, G. H. (2018). Digital breast tomosynthesis for breast cancer screening and diagnosis in women with dense breasts–a systematic review and meta-analysis. BMC cancer, 18, 1-9.

[6]. Ling, L., Aldoghachi, A. F., Chong, Z. X., Ho, W. Y., Yeap, S. K., et, al. (2022). Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms. International Journal of Molecular Sciences, 23(23), 15382.

[7]. Hameed, Z., Zahia, S., Garcia-Zapirain, B., Javier Aguirre, J., & Maria Vanegas, A. (2020). Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors, 20(16), 4373.

[8]. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25, 1315-1360.

[9]. Sikder, J., Das, U. K., & Chakma, R. J. (2021). Supervised learning-based cancer detection. International Journal of Advanced Computer Science and Applications, 12(5), 863-869.

[10]. Kumar, A., & Prateek, M. (2020). Automated Detection and Classification of Ki-67 Stained Nuclear Section Using Machine Learning Based on Texture of Nucleus to Measure Proliferation Score for Prognostic Evaluation of Breast Carcinoma, 1, 1-5.

[11]. Bhargava, H., Makeri, Y. A., Gyamenah, P., Gupta, S., Vyas, G., Sharma, A., & Chatterjee, S. (2022). BCRecommender System for Breast Cancer Diagnosis using Machine Learning Approaches, 1, 1-13.

[12]. Macedo, D. C., De Lima John, W. S., Santos, V. D., LO, M. T., et, al. (2022). Evaluating Interpretability in Deep Learning using Breast Cancer Histopathological Images. In 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images, 1, 276-281.

[13]. Anwar, F., Attallah, O., Ghanem, N., & Ismail, M. A. (2020). Automatic breast cancer classification from histopathological images. In 2019 International conference on advances in the emerging computing technologies, 1, 1-6.

[14]. Aksac, A., Demetrick, D. J., Ozyer, T., & Alhajj, R. (2019). BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC research notes, 12(1), 1-3.

[15]. Elston, C. W., & Ellis, I. (1991). I. The value of histological grade in breast cancer: experience from a large study with long‐term follow‐up. Pathological prognostic factors in breast cancer. Histopathology, 19, 403-410.

[16]. Bloom, H. J. G., & Richardson, W. (1957). Histological grading and prognosis in breast cancer: a study of 1409 cases of which 359 have been followed for 15 years. British journal of cancer, 11(3), 359.

Cite this article

Chen,M. (2023). Comparison of machine learning models on breast cancer risk prediction challenge. Applied and Computational Engineering,27,179-184.

Data availability

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

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

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

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Gabriel, C. A., & Domchek, S. M. (2010). Breast cancer in young women. Breast cancer research, 12, 1-10.

[2]. Nelson, H. D., Pappas, M., Cantor, A., Griffin, J., Daeges, M., & Humphrey, L. (2016). Harms of breast cancer screening: systematic review to update the 2009 US Preventive Services Task Force Recommendation. Annals of internal medicine, 164(4), 256-267.

[3]. Kerlikowske, K., Zhu, W., Tosteson, A. N., Sprague, B. L., Tice, J. A., et, al. (2015). Identifying women with dense breasts at high risk for interval cancer: a cohort study. Annals of internal medicine, 162(10), 673-681.

[4]. Tagliafico, A. S., Piana, M., Schenone, D., Lai, R., Massone, A. M., & Houssami, N. (2020). Overview of radiomics in breast cancer diagnosis and prognostication. The Breast, 49, 74-80.

[5]. Phi, X. A., Tagliafico, A., Houssami, N., Greuter, M. J., & de Bock, G. H. (2018). Digital breast tomosynthesis for breast cancer screening and diagnosis in women with dense breasts–a systematic review and meta-analysis. BMC cancer, 18, 1-9.

[6]. Ling, L., Aldoghachi, A. F., Chong, Z. X., Ho, W. Y., Yeap, S. K., et, al. (2022). Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms. International Journal of Molecular Sciences, 23(23), 15382.

[7]. Hameed, Z., Zahia, S., Garcia-Zapirain, B., Javier Aguirre, J., & Maria Vanegas, A. (2020). Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors, 20(16), 4373.

[8]. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25, 1315-1360.

[9]. Sikder, J., Das, U. K., & Chakma, R. J. (2021). Supervised learning-based cancer detection. International Journal of Advanced Computer Science and Applications, 12(5), 863-869.

[10]. Kumar, A., & Prateek, M. (2020). Automated Detection and Classification of Ki-67 Stained Nuclear Section Using Machine Learning Based on Texture of Nucleus to Measure Proliferation Score for Prognostic Evaluation of Breast Carcinoma, 1, 1-5.

[11]. Bhargava, H., Makeri, Y. A., Gyamenah, P., Gupta, S., Vyas, G., Sharma, A., & Chatterjee, S. (2022). BCRecommender System for Breast Cancer Diagnosis using Machine Learning Approaches, 1, 1-13.

[12]. Macedo, D. C., De Lima John, W. S., Santos, V. D., LO, M. T., et, al. (2022). Evaluating Interpretability in Deep Learning using Breast Cancer Histopathological Images. In 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images, 1, 276-281.

[13]. Anwar, F., Attallah, O., Ghanem, N., & Ismail, M. A. (2020). Automatic breast cancer classification from histopathological images. In 2019 International conference on advances in the emerging computing technologies, 1, 1-6.

[14]. Aksac, A., Demetrick, D. J., Ozyer, T., & Alhajj, R. (2019). BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC research notes, 12(1), 1-3.

[15]. Elston, C. W., & Ellis, I. (1991). I. The value of histological grade in breast cancer: experience from a large study with long‐term follow‐up. Pathological prognostic factors in breast cancer. Histopathology, 19, 403-410.

[16]. Bloom, H. J. G., & Richardson, W. (1957). Histological grading and prognosis in breast cancer: a study of 1409 cases of which 359 have been followed for 15 years. British journal of cancer, 11(3), 359.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.