A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.

ABSTRACT:

Background

Machine learning methodologies are gaining popularity for developing medical prediction models for datasets with a large number of predictors, particularly in the setting of clustered and longitudinal data. Binary Mixed Model (BiMM) forest is a promising machine learning algorithm which may be applied to develop prediction models for clustered and longitudinal binary outcomes. Although machine learning methods for clustered and longitudinal methods such as BiMM forest exist, feature selection has not been analyzed via data simulations. Feature selection improves the practicality and ease of use of prediction models for clinicians by reducing the burden of data collection. Thus, feature selection procedures are not only beneficial, but are often necessary for development

SUBMITTER: Speiser JL 

PROVIDER: S-EPMC8131242 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature