Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.
Background: Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53-0.64). Machine learning (ML) of...
| Publicado en: | Breast Cancer Research Vol. 21; no. 1 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
6/20/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |