Application of Machine Learning techniques to improve detection, diagnosis & prediction of breast cancer: A Comparative Analysis
Abstract
There is an increasing cancer burden in India across ages and sexes. The most significant cancer incident rate in females is ‘Breast Cancer’. Early detection and treatment are the key to lower mortality rate and better survival rates for cancer patients in the country. This review paper provides an understanding of the various types of breast cancers, their symptoms, causes, current detection and diagnosis methods. The paper presents different Machine Learning (ML) techniques that are in development for the detection and diagnosis of breast cancer. The objective of the paper is to highlight outcomes of some select previous studies between 2016 to 2020 using various ML techniques and summarize the selected algorithms which can be used for breast cancer prediction and diagnosis. In our paper, we have also made an attempt to implement the Convolutional Neural Networks (CNN) model on the Breast Cancer Wisconsin (Diagnostic) dataset, whose results are presented and discussed.
Keywords:
machine learning (ML), deep learning (DL), breast cancer prediction, datasets
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References
1. Hazra, A., Mandal, S.K., Gupta, A.: Study and analysis of breast cancer cell detection using Naïve Bayes, SVM and Ensemble Algorithms. Int. J. Comput. Appl. 145, 0975–8887 (2016)
2. Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S.: Predicting breast cancer recurrence using effective classification and feature selection technique. In: 19th International Conference on Computer and Information Technology (ICCIT), pp. 310–314. IEEE (2016)
3. Asri, H., Mousannif, H., Al, M.H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83, 1064–1069 (2016)
4. Mohammed, S.A., Darrab, S., Noaman, S.A., Saake, G. (2020). Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. In: Tan, Y., Shi, Y., Tuba, M. (eds) Data Mining and Big Data. DMBD 2020. Communications in Computer and Information Science, vol 1234. Springer, Singapore. https://doi.org/10.1007/978-981-15-7205-0_10
5. Silva, J., Lezama, O.B.P., Varela, N., Borrero, L.A. (2019). Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds) Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science(), vol 11484. Springer, Cham. https://doi.org/10.1007/978-3-030-19223-5_2
6. Anuranjeeta, K. K. Shukla, A. Tiwari, and S. Sharma, “Classification of Histopathological Images of Breast Cancerous and Non Cancerous Cells based on Morphological Features,” Biomedical and Pharmacology Journal, vol. 10, no. 1, pp. 353–366, 2017.
7. A.-A. Nahid, M. A. Mehrabi, and Y. Kong, “Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering,” BioMed Research International, vol. 2018, pp. 1–20, 2018.
8. Alzubaidi, O. Al-Shamma, M. A. Fadhel, L. Farhan, J. Zhang, and Y. Duan, “Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model,” Electronics, vol. 9, no. 3, p. 445, 2020.
9. Ray, A. A. Abdullah, D. K. Mallick, and S. Ranjan Dash, “Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets,” Journal of Physics: Conference Series, vol. 1372, p. 012062, 2019.
10. Classification of Breast Cancer using Fast Fuzzy Clustering based on Kernel,” IOP Conference Series: Materials Science and Engineering
11. IBM Cloud Education, "What is Machine Learning?", Ibm.com, 2015. [Online]. Available: https://www.ibm.com/cloud/learn/machine-learning#toc-real-world-Lyja9GSr.
12. "Supervised learning and other machine learning tasks", SuperAnnotate Blog, 2021. [Online]. Available: https://blog.superannotate.com/supervised-learning-and-other-machine-learning-tasks/.
13. J. Delua, "Supervised vs. Unsupervised Learning: What’s the Difference?", Ibm.com, 2021. [Online]. Available: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning.
14. "AI & Machine Learning", Simplilearn, 2022. [Online]. Available: https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm.
15. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, 2021.
16. Ferlay, M. Colombet, I. Soerjomataram, C. Mathers, D. M. Parkin, M. Piñeros, A. Znaor, and F. Bray, “Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods,” International Journal of Cancer, vol. 144, no. 8, pp. 1941–1953, 2018.
17. "Statistics of Breast Cancer In India | Cytecare Hospitals", Cytecare Hospital in Bangalore, 2022. [Online]. Available: https://cytecare.com/blog/statistics-of-breast-cancer/#:~:text=One%20in%20twenty%2Deight%20Indian,group%20(1%20in%2060).
18. "Understanding Your Breast Cancer Diagnosis", Cancer.org. [Online]. Available:https://www.cancer.org/cancer/breast-cancer/understanding-a-breast-cancer-diagnosis.html.
19. "Breast Cancer Early Detection and Diagnosis | How To Detect Breast Cancer", Cancer.org.[Online].Available: https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection.html.
20. "Breast Cancer Overview: Causes, Symptoms, Signs, Stages & Types", Cleveland Clinic. [Online]. Available: https://my.clevelandclinic.org/health/diseases/3986-breast-cancer.
21. UCI Machine Learning Repository (Center for Machine Learning and Intelligent Systems) https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
22. A. Doran, Cambridgefinsights.com, 2020. [Online]. Available: https://www.cambridgefinsights.com/post/data-science-in-asset-management-a-game-changer.
2. Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S.: Predicting breast cancer recurrence using effective classification and feature selection technique. In: 19th International Conference on Computer and Information Technology (ICCIT), pp. 310–314. IEEE (2016)
3. Asri, H., Mousannif, H., Al, M.H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83, 1064–1069 (2016)
4. Mohammed, S.A., Darrab, S., Noaman, S.A., Saake, G. (2020). Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. In: Tan, Y., Shi, Y., Tuba, M. (eds) Data Mining and Big Data. DMBD 2020. Communications in Computer and Information Science, vol 1234. Springer, Singapore. https://doi.org/10.1007/978-981-15-7205-0_10
5. Silva, J., Lezama, O.B.P., Varela, N., Borrero, L.A. (2019). Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds) Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science(), vol 11484. Springer, Cham. https://doi.org/10.1007/978-3-030-19223-5_2
6. Anuranjeeta, K. K. Shukla, A. Tiwari, and S. Sharma, “Classification of Histopathological Images of Breast Cancerous and Non Cancerous Cells based on Morphological Features,” Biomedical and Pharmacology Journal, vol. 10, no. 1, pp. 353–366, 2017.
7. A.-A. Nahid, M. A. Mehrabi, and Y. Kong, “Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering,” BioMed Research International, vol. 2018, pp. 1–20, 2018.
8. Alzubaidi, O. Al-Shamma, M. A. Fadhel, L. Farhan, J. Zhang, and Y. Duan, “Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model,” Electronics, vol. 9, no. 3, p. 445, 2020.
9. Ray, A. A. Abdullah, D. K. Mallick, and S. Ranjan Dash, “Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets,” Journal of Physics: Conference Series, vol. 1372, p. 012062, 2019.
10. Classification of Breast Cancer using Fast Fuzzy Clustering based on Kernel,” IOP Conference Series: Materials Science and Engineering
11. IBM Cloud Education, "What is Machine Learning?", Ibm.com, 2015. [Online]. Available: https://www.ibm.com/cloud/learn/machine-learning#toc-real-world-Lyja9GSr.
12. "Supervised learning and other machine learning tasks", SuperAnnotate Blog, 2021. [Online]. Available: https://blog.superannotate.com/supervised-learning-and-other-machine-learning-tasks/.
13. J. Delua, "Supervised vs. Unsupervised Learning: What’s the Difference?", Ibm.com, 2021. [Online]. Available: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning.
14. "AI & Machine Learning", Simplilearn, 2022. [Online]. Available: https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm.
15. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, 2021.
16. Ferlay, M. Colombet, I. Soerjomataram, C. Mathers, D. M. Parkin, M. Piñeros, A. Znaor, and F. Bray, “Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods,” International Journal of Cancer, vol. 144, no. 8, pp. 1941–1953, 2018.
17. "Statistics of Breast Cancer In India | Cytecare Hospitals", Cytecare Hospital in Bangalore, 2022. [Online]. Available: https://cytecare.com/blog/statistics-of-breast-cancer/#:~:text=One%20in%20twenty%2Deight%20Indian,group%20(1%20in%2060).
18. "Understanding Your Breast Cancer Diagnosis", Cancer.org. [Online]. Available:https://www.cancer.org/cancer/breast-cancer/understanding-a-breast-cancer-diagnosis.html.
19. "Breast Cancer Early Detection and Diagnosis | How To Detect Breast Cancer", Cancer.org.[Online].Available: https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection.html.
20. "Breast Cancer Overview: Causes, Symptoms, Signs, Stages & Types", Cleveland Clinic. [Online]. Available: https://my.clevelandclinic.org/health/diseases/3986-breast-cancer.
21. UCI Machine Learning Repository (Center for Machine Learning and Intelligent Systems) https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
22. A. Doran, Cambridgefinsights.com, 2020. [Online]. Available: https://www.cambridgefinsights.com/post/data-science-in-asset-management-a-game-changer.
Published
21/12/2023
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How to Cite
Shrotriya, S., P K, N. B., & Kulkarni, A. (2023). Application of Machine Learning techniques to improve detection, diagnosis & prediction of breast cancer: A Comparative Analysis. Journal of Innovations in Applied Pharmaceutical Science (JIAPS), 8(3), 57-63. https://doi.org/10.37022/jiaps.v8i3.519
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