Risk of IgE-mediated food allergy and its impact on child growth: A machine learning approach.

ABSTRACT:

Objective

Food allergy (FA) directly affects children's nutritional status, with a significantly higher risk of growth retardation among affected children. Identifying risk factors for FA and strategies to promote growth catch-up can offer valuable guidance for the treatment and nutritional management of children with FA.

Design

We developed machine learning models to predict the occurrence of immunoglobulin E-mediated food allergy (IgE-FA) and the likelihood of post-treatment growth catch-up, using demographic and biological baseline data.

Patients

We recruited 130 children aged 0-3 years with IgE-FA as the FA group and 65 healthy children as the control group.

Results

Using machine-learning-based bioinformatics analysis, we developed predictive models and ide

SUBMITTER: Zhu N 

PROVIDER: S-EPMC12280336 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature