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Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction.

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Peer-reviewed

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Abstract

A recent paper [1] compared two well-known breast cancer risk prediction models (BCRAT and BOADICEA) with eight different machine learning (ML) methods. The authors found a striking improvement in cancer prediction with ML. While their comparative assessment against more classical approaches is timely, we are skeptical about the results presented.

Description

Journal Title

Breast Cancer Research

Conference Name

Journal ISSN

1465-542X
1465-542X

Volume Title

22

Publisher

Springer Nature

Rights and licensing

Except where otherwised noted, this item's license is described as Attribution 4.0 International
Sponsorship
Cancer Research UK (20861)