{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:03:04Z","timestamp":1787022184458,"version":"build-2736575974"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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 models were further validated using the 2023 NHIS data. We examined the associations of potential risk factors identified from both Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) with asthma in youth. Between the different sampling techniques, undersampling the major class (subjects without asthma) yielded the best results in terms of the area under the curve (AUC) and F1 scores for the different predictive models. The Logistic Regression performed the best with the under-sampled data, yielding an AUC score of 0.7654 and an F1 score of 0.3452. Beside of some well-known risk factors for asthma development, such as gender and socioeconomic status, we have identified additional potential factors associated with asthma development in youth such as \u201ctook prescription medication in past 12 months\u201d, \u201cage\u201d and \u201cgeneral health status\u201d which had the highest magnitude mean Shapley Additive exPlanations (SHAP) values of 0.094, 0.076 and 0.042. This study successfully built machine learning models to predict asthma development in youth with good model performance. This will be important for early screening and detection of asthma in youth.<\/jats:p>","DOI":"10.1371\/journal.pone.0336591","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T18:30:41Z","timestamp":1762972241000},"page":"e0336591","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting the risk of asthma development in youth using machine learning 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