Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

Abstract 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...

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Bibliographic Details
Main Authors: Chang Ming, Valeria Viassolo, Nicole Probst-Hensch, Pierre O. Chappuis, Ivo D. Dinov, Maria C. Katapodi
Format: Article
Language:English
Published: BMC 2019-06-01
Series:Breast Cancer Research
Subjects:
Breast cancer
Risk prediction
Machine learning
Big data
Personalized medicine
Cancer screening
Online Access:http://link.springer.com/article/10.1186/s13058-019-1158-4

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http://link.springer.com/article/10.1186/s13058-019-1158-4

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