A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study.

ABSTRACT:

Background

Existing diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation.

Methods

BN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline [73% women; mean (standard deviation) age = 61.0 (6.9) years]. Participants were randomly assigned to a training (n = 12,748) set and a validation (n = 3,186) set. Model performances were assessed using area under the receiver operating characteristic curve (AUC).

Results

During an average follow-up of 4.1 (interquartile range = 3.3-4.5) years, 1,302 (8.17%) participants devel

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9382298 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature