An evaluation of synthetic data augmentation for mitigating covariate bias in health data.

ABSTRACT: Data bias is a major concern in biomedical research, especially when evaluating large-scale observational datasets. It leads to imprecise predictions and inconsistent estimates in standard regression models. We compare the performance of commonly used bias-mitigating approaches (resampling, algorithmic, and post hoc approaches) against a synthetic data-augmentation method that utilizes sequential boosted decision trees to synthesize under-represented groups. The approach is called synthetic minority augmentation (SMA). Through simulations and analysis of real health datasets on a logistic regression workload, the approaches are evaluated across various bias scenarios (types and severity levels). Performance was assessed based on area under the curve, calibration (Brier score), precision of

SUBMITTER: Juwara L 

PROVIDER: S-EPMC11026977 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature