Early Detection of Breast Cancer Based on Patient Symptom Data Using Naive Bayes Algorithm on Genomic Data
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48465-0_64
Reference16 articles.
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2. Burnside, E.S., et al.: Comparing mammography abnormality features to genetic variants in the prediction of breast cancer in women recommended for breast biopsy. Acad. Radiol. 23(1), 62–69 (2016). https://doi.org/10.1016/j.acra.2015.09.007
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4. Kharya, S., Soni, S.: Weighted Naive Bayes classifier: a predictive model for breast cancer detection. Int. J. Comput. Appl. 133(9), 32–37 (2016). https://doi.org/10.5120/ijca2016908023
5. Cain, E.H., Saha, A., Harowicz, M.R., Marks, J.R., Marcom, P.K., Mazurowski, M.A.: Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast Cancer Res. Treat. 173(2), 455–463 (2019). https://doi.org/10.1007/s10549-018-4990-9
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