A nonparametric method for classification trees using grouped covariates.

ABSTRACT: A group of variables are commonly seen in diagnostic medicine when multiple prognostic factors are aggregated into a composite score to represent the risk profile. A model selection method considers these covariates as all-in or all-out types. Model selection procedures for grouped covariates and their applications have thrived in recent years, in part because of the development of genetic research in which gene-gene or gene-environment interactions and regulatory network pathways are considered groups of individual variables. However, little has been discussed on how to utilize grouped covariates to grow a classification tree. In this paper, we propose a nonparametric method to address the selection of split variables for grouped covariates and their following selection of split points. C

SUBMITTER: Lin FC 

PROVIDER: S-EPMC9925394 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature