Evolutionary Feature-Binning with Adaptive Burden Thresholding for Biomedical Risk Stratification

Abstract

Multivariate associations including additivity, feature interactions, heterogeneous effects, and rare feature states can present significant obstacles in statistical and machine-learning analyses. These relationships can limit the detection capabilities of many analytical methodologies when predicting outcomes including risk stratification in biomedical survival analyses. Feature Inclusion Bin Evolver for Risk Stratification (FIBERS) was previously proposed using an evolutionary algorithm to discover groups (i.e. bins) of features wherein the burden of feature values automatically determined the risk strata of a given instance in right-censored survival analysis. A key limitation of FIBERS is that it assumes a fixed threshold for feature burden in stratifying high vs. low risk, which restricts the flexibility of bin discovery. In the present work, we extend FIBERS to include different strategies for adaptive burden thresholding such that feature bins are discovered alongside the threshold that best separates risk strata. Preliminary comparative performance evaluation was conducted across simulated datasets with different underlying ideal burden thresholds yielding performance improvements over the original FIBERS algorithm. This algorithmic feasibility study lays the groundwork for ongoing application to the real-world problem of kidney graft failure risk stratification in dealing with the expected population heterogeneity including differences in race, ethnicity, and sex.