Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank.

ABSTRACT: We aimed to identify potential novel predictors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We utilised an analysis pipeline where machine learning was used for feature selection, prior to risk prediction by classical statistical models. An "extreme gradient boosting" (XGBoost) machine with Shapley feature-importance measures were used for feature selection among [Formula: see text] 1.7 k features in 104,313 post-menopausal women from the UK Biobank. We constructed and compared the "augmented" Cox model (incorporating the two PRS, known and novel predictors) with a "baseline" Cox model (incorporating the two PRS and known predictors) for risk prediction. Both of the two PRS were significant in th

SUBMITTER: Liu X 

PROVIDER: S-EPMC10247810 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature