ArticlePediatric research2026
Ensemble model-based prediction of school-age asthma risk in children with food allergy.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEarly-life food allergy (FA) is a major childhood asthma risk, but prospective screening tools for high-risk infants aged 0-2 are lacking, delaying early intervention.
objectiveTo develop an ensemble-learning model with cost-sensitive optimization for early asthma risk screening in FA infants (0-2 years) and provide quantitative clinical decision support.
methodsThis prospective cohort enrolled 447 FA-confirmed infants (0-2 years), collecting 135 baseline predictors across 7 categories. After median 4.5-year follow-up to school age (≥6 years), we generated 3 clinically interpretable features and built a two-stage weighted ensemble (Random Forest, XGBoost, LightGBM, Deep Residual Networks) with cost-sensitive learning (missed-to-false-alarm ratio = 5:1) to optimize thresholds.
resultsOn the independent test set, AUC = 0.886, recall = 1.00 (zero missed). Decision-curve analysis showed superior net benefit across broad thresholds, including optimal cutoff 0.09. Top predictors: RSV infection, wheeze frequency, nocturnal sleep, antibiotic exposure, and rhinitis duration. Sensitivity analysis confirmed stable zero missed at cost ratios ≥5:1, indicating 5:1 was not the key driver.
conclusionThe model preliminarily predicts school-age asthma risk in FA infants. Its high-recall design avoids missed diagnoses and offers quantitative references for early intervention and individualized follow-up before age 2. IMPACT: Core finding: A performance-weighted ensemble model predicts school-age asthma in food-allergic infants (0-2 years) with an AUC of 0.886 and recall 1.00 using only clinical data. Contribution: A cost-sensitive, feature-engineered model tailored to this high-risk population; identifies RSV, wheeze grading, and antibiotic load as top predictors. SIGNIFICANCE: Enables early, non-invasive screening before age 2, avoids missed high-risk children, and supports targeted preventive interventions in primary care.
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