Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study.

ABSTRACT:

Background

Generalized linear models have been favored in healthcare research due to their interpretability. In contrast, tree-based models, such as random forest or boosted trees, are often preferred in machine learning (ML) and commercial settings due to their strong predictive performance. However, for clinical applications, model interpretability remains essential for actionable results and patient understanding. This study used ML to detect cognitive decline for the purpose of timely screening and uncovering associations with psychosocial determinants. All models were interpreted to enhance transparency and understanding of their predictions.

Methods

Data from the 2018 to 2020 Health and Retirement Study was used to create three linear regression models and three tree-b

SUBMITTER: Ishikawa KM 

PROVIDER: S-EPMC12652623 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature