Deep Survey Analysis of Diagnosis of Breast Cancer
| Autor: | K. Vijila Rani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| ISSN: | 1937-688X |
| DOI: | 10.5281/zenodo.3714292 |
| Popis: | Machine learning technology is progressing rapidly in decision-making in the field of healthcare. Machine learning techniques and massive data analysis improve the precision of decision-making from diagnosis to treatment. Specific machine learning algorithms are discussed, used to predict breast cancer. The approach, methods of assessment and outcomes are checked. This consolidates efforts made by many health diagnosis researchers, especially in the field of diagnosis of breast cancer. {"references":["J. Dheeba, N. Albert Singh, S. Tamil Selvi (2014), \"Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach\", J. of Biomed. Inform., Volume 49, pp. 45–52, DOI: 10.1016/j.jbi.2014.01.010","National Cancer Institute Breast Cancer, Available from: http://www.cancer.gov/cancertopics/types/breast","Luanyi Yang, Zeshui Xu (2017), \"Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning\", Int. J. of Mach. Learn. and Cybernet., Volume 10, Issue 3, pp. 591–601","Breast Cancer Organization (2018), \"Types of Breast Cancer\", Available from: http://www.breastcancer.org/symptoms/types","Cuong Nguyen, Yong Wang, Ha Nam Nguyen (2013), \"Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic\", J. of Biomed. Sci. and Eng., ISSN: 1937-688X, Volume 6, Issue 5, pp. 551–560, DOI: 10.4236/jbise.2013.65070"]} |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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