The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis
Open accessAbstract
Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.
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Cites
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Cited by
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040
RetractedBreast Cancer Prediction: A Comparative Study Using Machine Learning Techniques
Islam, M. M., Haque, M. R., Iqbal, H., Hasan, M. M., Hasan, M., & Kabir, M. N. (2020) · SN Computer Science · doi:10.1007/s42979-020-00305-w · Cited by 361
Artificial intelligence for aging and longevity research: Recent advances and perspectives
Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.003 · Cited by 246
“These methods were also reviewed in (Yue et al., 2018).”
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236
A systematic review of artificial intelligence techniques in cancer prediction and diagnosis
Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09648-w · Cited by 223
Metadata
- Authors
- Yue, W., Wang, Z., Chen, H., Payne, A., & Liu, X.
- Venue
- Designs
- Publisher
- MDPI AG
- Published
- 2018-05-09
- Volume
- 2
- Issue
- 2
- Pages
- 13
- ISSN
- 2411-9660 (ISSN-L)
- OpenAlex
- W2800002784
- Cited by
- 303
- References
- 10
- FWCI
- 22.50
- Type
- journal article
- Language
- en
- Open access
- gold
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