The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Comparative study of machine learning algorithms for breast cancer detection and diagnosis
Abstract
Breast cancer is one of the most widespread diseases among women in the UAE and worldwide. Correct and early diagnosis is an extremely important step in rehabilitation and treatment. However, it is not an easy one due to several uncertainties in detection using mammograms. Machine Learning (ML) techniques can be used to develop tools for physicians that can be used as an effective mechanism for early detection and diagnosis of breast cancer which will greatly enhance the survival rate of patients. This paper compares three of the most popular ML techniques commonly used for breast cancer detection and diagnosis, namely Support Vector Machine (SVM), Random Forest (RF) and Bayesian Networks (BN). The Wisconsin original breast cancer data set was used as a training set to evaluate and compare the performance of the three ML classifiers in terms of key parameters such as accuracy, recall, precision and area of ROC. The results obtained in this paper provide an overview of the state of art ML techniques for breast cancer detection.
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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
Cited by
A Review of Machine Learning and Deep Learning Applications
Shinde, P. P., & Shah, S. (2018) · 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) · doi:10.1109/iccubea.2018.8697857 · Cited by 773
Metadata
- Authors
- Bazazeh, D., & Shubair, R.
- Venue
- 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 1-4
- OpenAlex
- W2579213716
- Cited by
- 188
- References
- 2
- FWCI
- 16.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bazazeh, D., & Shubair, R. (2016). Comparative study of machine learning algorithms for breast cancer detection and diagnosis. 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA). https://doi.org/10.1109/icedsa.2016.7818560
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