Machine Learning-Based Prediction of Food Allergy in Children Aged <2 Years with Atopic Dermatitis.

Background/Objectives: Children with atopic dermatitis (AD) are at increased risk of food allergy (FA). This study aimed to develop and evaluate machine learning (ML) models for predicting FA in children aged <2 years with AD and to assess feature contribution. Methods: This retrospective study included children aged 1 month to 2 years who were diagnosed with AD and underwent evaluation for FA. Demographic characteristics, clinical features, and laboratory findings were extracted from electronic medical records. Four ML algorithms-CatBoost, XGBoost, LightGBM, and logistic regression-were evaluated for predicting FA. Results: A total of 435 children with AD were included, of whom 185 (42.5%) were female. The median age at presentation was 7.4 months (IQR, 4.8-10.8), and FA was present in 101 patients (23.2%). Among all evaluated models, CatBoost using clinical features alone demonstrated the best overall performance, achieving an AUC of 0.91 (95% CI, 0.84-0.97), with a sensitivity of 0.85 and a specificity of 0.91. Using combined clinical and laboratory features, XGBoost achieved an AUC of 0.88 (95% CI, 0.79-0.95), with a sensitivity of 0.70 and a specificity of 0.96. Using clinical features alone, LightGBM and logistic regression achieved AUCs of 0.89 (95% CI, 0.82-0.96) and 0.86 (95% CI, 0.75-0.95), respectively, with sensitivities of 0.85 and 0.65 and specificities of 0.82 and 0.88. Conclusions: CatBoost demonstrated the best overall performance in predicting FA in children aged <2 years with AD using clinical features alone. ML models may help identify children with AD who require further diagnostic evaluation for FA.