The efficacy prediction of subcutaneous immunotherapy for pediatric allergic Rhinitis: Application of machine learning methods

Abstract

Allergen immunotherapy (AIT) is an effective treatment for allergic rhinitis (AR); however, some patients do not respond optimally. This study aims to identify the factors influencing the efficacy of subcutaneous specific immunotherapy (SCIT) for AR in children and to develop a predictive model for treatment outcomes using machine learning techniques. Data were collected from 272 children aged 4–15 years with AR, excluding those with asthma, who underwent more than three years of mite SCIT at two hospitals in southern Zhejiang. Patients were categorized into effective and ineffective groups based on the improvement in the Combined Symptom Medication Score (CSMS) before and after treatment. The data were split into a training set and a testing set, and the bIRSCA algorithm was applied to identify optimal feature subsets in the training set. The selected features were then used in a support vector machine (SVM) model to assess performance on the testing set, with ten-fold cross-validation applied to evaluate the model. The final results were based on the average performance metrics across ten iterations. The bIRSCA-SVM model identified key biomarkers, including the sIgE/tIgE (Der p) ratio, sIgE (Der p), eosinophil count, eosinophil ratio, and the sIgE/tIgE (Der f) ratio, as significant predictors of therapeutic efficacy. The model achieved an accuracy of 88.992 %, sensitivity of 99.736 %, and specificity of 86.872 %, outperforming other models. In conclusion, a positive response to SCIT is associated with baseline levels of the identified biomarkers. The bIRSCA-SVM model provides an effective and accurate method for predicting the efficacy of mite SCIT in children with allergic rhinitis.