Repository logo
 

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

Published version
Peer-reviewed

Type

Article

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

Volume Title

22

Publisher

Springer Nature