Letter to the editor: a response to Ming’s study on machine learning techniques for personalized breast cancer risk prediction
Daniele Giardiello, Antonis C. Antoniou, Luigi Mariani, Douglas F. Easton, Ewout Willem Steyerberg · Breast Cancer Research · 2020
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.