Comparing penalization methods for linear models on large observational health data.
Egill Axfjord FridgeirssonRoss D WilliamsPeter RijnbeekMarc A SuchardJenna Marie RepsPublished in: Journal of the American Medical Informatics Association : JAMIA (2024)
L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.