Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction.
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Giardiello, Daniele
Antoniou, Antonis C
Mariani, Luigi
Easton, Douglas F
Steyerberg, Ewout W
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
Keywords
Breast, Breast Neoplasms, Humans, Machine Learning, Risk
Journal Title
Breast Cancer Research
Conference Name
Journal ISSN
1465-542X
1465-542X
1465-542X
Volume Title
22
Publisher
Springer Nature