Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

ABSTRACT: We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37-73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP valu

SUBMITTER: Madakkatel I 

PROVIDER: S-EPMC8626442 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature