Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches.
Journal:
JCO clinical cancer informatics
Published Date:
Feb 1, 2022
Abstract
PURPOSE: The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, such as the Gail model for breast cancer, which are easy to interpret, but may theoretically be less accurate than advanced machine learning (ML) models.