Predicting the risk of asthma development in youth using machine learning models.

ABSTRACT: Asthma is a chronic respiratory disease characterized by wheezing and difficulty breathing, which disproportionally affects 4.7 million children in the U.S. Currently, there is a lack of asthma predictive models for youth with good performance. This study aims to build machine learning models to better predict asthma development in youth using easily accessible national survey data. We analyzed cross-sectional combined 2021 and 2022 National Health Interview Survey (NHIS) data from 9,716 youth subjects with their corresponding parent information. We built several machine learning models with various sampling techniques (under- or over-sampling) for asthma prediction in youth, including XGBoost, Neural Networks, Random Forest, Support Vector Machine (SVM), and Logistic Regression. These mod

SUBMITTER: Xie M 

PROVIDER: S-EPMC12611137 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature