Machine learning‐based prediction of inborn errors of immunity in patients with low IgE levels

Abstract Background Low total serum IgE has emerged as a potential marker of inborn errors of immunity (IEI), but no predictive tool exists to stratify risk in pediatric patients. Methods In this retrospective study, 677 children with IgE <2.5 IU/mL were analyzed. We handled missing data with mean‐value imputation, applied SMOTE to address class imbalance, and conducted feature selection via ANOVA F‐test and SelectKBest. Ten machine‐learning models were trained and tuned using nested five‐fold cross‐validation (5 × 5 repeats). Primary evaluation metrics included sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results The Random Forest classifier achieved the highest performance (AUROC 0.86; sensitivity 0.81; specificity 0.75). Key predictors included lymphocyte, platelet, and neutrophil counts, mean platelet volume, and C‐reactive protein. Conclusion Our data‐driven framework accurately identifies children at risk for IEI using routine laboratory parameters. Prospective external validation and integration into clinical workflows are warranted to facilitate early diagnosis.