Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study.

ABSTRACT:

Background

Asthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.

SUBMITTER: Ahmadiankalati M 

PROVIDER: S-EPMC11529447 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature