Interpretable machine learning for allergic rhinitis prediction among preschool children in Urumqi, China.

ABSTRACT: This study aimed to investigate the advantages and applications of machine learning models in predicting the risk of allergic rhinitis (AR) in children aged 2-8, compared to traditional logistic regression. The study analyzed questionnaire data from 7131 children aged 2-8, which was randomly divided into training, validation, and testing sets in a ratio of 55:15:30, repeated 100 times. Predictor variables included parental allergy, medical history during the child's first year (cfy), and early life environmental factors. The time of first onset of AR was restricted to after the age of 1 year to establish a clear temporal relationship between the predictor variables and the outcome. Feature engineering utilized the chi-square test and the Boruta algorithm, refining the dataset for analysis.

SUBMITTER: Wang J 

PROVIDER: S-EPMC11437280 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature