The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic
Open accessAbstract
As the incidence of this disease has increased significantly in the recent years, expert systems and machine learning techniques to this problem have also taken a great attention from many scholars. This study aims at diagnosing and prognosticating breast cancer with a machine learning method based on random forest classifier and feature selection technique. By weighting, keeping useful features and removing redundant features in datasets, the method was obtained to solve diagnosis problems via classifying Wisconsin Breast Cancer Diagnosis Dataset and to solve prognosis problem via classifying Wisconsin Breast Cancer Prognostic Dataset. On these datasets we obtained classification accuracy of 100% in the best case and of around 99.8% on average. This is very promising compared to the previously reported results. This result is for Wisconsin Breast Cancer Dataset but it states that this method can be used confidently for other breast cancer diagnosis problems, too.
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Cited by
Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338
Prostate cancer detection using deep convolutional neural networks
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Metadata
- Authors
- Nguyen, C., Wang, Y., & Nguyen, H. N.
- Venue
- Journal of Biomedical Science and Engineering
- Publisher
- Scientific Research Publishing, Inc.
- Published
- 2013
- Volume
- 06
- Issue
- 05
- Pages
- 551-560
- ISSN
- 1937-6871 (ISSN-L)
- 1937-688X
- OpenAlex
- W1994307947
- Cited by
- 310
- References
- 25
- FWCI
- 4.35
- Type
- journal article
- Language
- en
- Open access
- diamond
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