Explainable Machine Learning to Identify Clinical, Environmental, and Social Factors Associated with Diagnosed Pediatric Asthma.

BACKGROUND: Pediatric asthma reflects clinical, physiologic, environmental, and social determinants, yet most classification models use clinical variables alone and are difficult to interpret. OBJECTIVE: To use explainable machine learning on nationally representative data to identify and rank factors associated with a reported physician diagnosis of asthma in U.S. children, and to test whether fewer higher-ranked features retain full performance. METHODS: We analyzed 6,567 children aged 6 to 17 years in NHANES 2007-2012 (weighted asthma prevalence 18.8%). Of 477 harmonized variables, 22 entered the primary CatBoost model after pre-specified eligibility screening, feature engineering, and correlation pruning. Data were split into training, validation, and held-out internal test sets (60/20/20), with the operating threshold locked on validation. SHAP ranked features; 95% confidence intervals used stratified bootstrap. RESULTS: The primary model reached a test-set AUC of 0.78 (95% CI 0.74-0.81), sensitivity 0.75, specificity 0.63, and negative predictive value 0.92. The highest-ranked features included recent wheezing, family history of asthma, general health status, serum cotinine, household size, FEV₁/FVC ratio, race/Hispanic origin, insurance coverage, and U.S. birth. A reduced model of these ten plus two spirometry-availability indicators had more favorable point estimates on every metric (AUC 0.80; paired difference 0.023, 95% CI 0.007-0.041; sensitivity 0.79; specificity 0.67; negative predictive value 0.93). CONCLUSION: Explainable machine learning recovers established clinical signals and ranks social and contextual factors associated with a reported pediatric asthma diagnosis; a parsimonious feature set retains this performance. Given the cross-sectional design and internal evaluation, external and prospective validation is required before clinical use.