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.