Subgroup-aware suicide risk prediction via functionally adaptive interaction regularized regression

Abstract Suicide is a major public health issue, and machine learning offers promising tools to identify individuals at heightened risk. However, conventional models often fail to optimize performance across population subgroups, which can lead to inconsistent detection and outcomes. Using 2013–2023 data from the U.S. National Survey on Drug Use and Health (NSDUH), this study introduces the Functionally Adaptive Interaction Regularization-Penalized Logistic Regression (FAIR-PLR) framework: a logistic extension of the linear FAIR framework that combines group-covariate interaction terms with subgroup-size-weighted elastic-net regularization. Benchmarked against standard logistic regression, pooled elastic net with uniform weights, a decision tree, and subgroup-specific PLR across single subgroups (age, sex, race, BMI, insurance, rurality) and cross-sectional strata of psychological distress and treatment, FAIR-PLR matches or improves upon every baseline in overall performance while narrowing subgroup-level gaps in AUC, true positive rate, and precision at top-risk thresholds. FAIR-PLR gives a subgroup-balanced tool for national-scale suicide risk stratification.