Utilizing machine learning to predict participant response to follow-up health surveys in the Millennium Cohort Study.

ABSTRACT: The Millennium Cohort Study is a longitudinal study which collects self-reported data from surveys to examine the long-term effects of military service. Participant nonresponse to follow-up surveys presents a potential threat to the validity and generalizability of study findings. In recent years, predictive analytics has emerged as a promising tool to identify predictors of nonresponse. Here, we develop a high-skill classifier using machine learning techniques to predict participant response to follow-up surveys of the Millennium Cohort Study. Six supervised algorithms were employed to predict response to the 2021 follow-up survey. Using latent class analysis (LCA), we classified participants based on historical survey response and compared prediction performance with and without this var

SUBMITTER: Barkho W 

PROVIDER: S-EPMC11519444 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature