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Pediatr Allergy Immunol 2025[Dec]; 36 (12): e70258 PMID41351317show ga
INTRODUCTION: Asthma is a common childhood disease with symptoms such as cough, wheezing, and shortness of breath. This study evaluated the role of artificial intelligence in improving diagnostic accuracy in children. METHODS: We included patients aged 6-18 years evaluated at our clinic between January 2024 and January 2025. Those with chronic cough were classified as asthma or non-asthma based on final diagnosis. Demographic, clinical, and pulmonary function data were collected. Eight machine learning models Gradient Boosting, AdaBoost, Random Forest, Logistic Regression, Linear Discriminant Analysis, Decision Tree, k-Nearest Neighbors, and Naive Bayes were applied, and their performance was assessed using accuracy, precision, recall, F1 score, ROC AUC, and MCC. RESULTS: A total of 900 children were included, with 450 diagnosed with asthma and 450 with non-asthmatic chronic cough. Males comprised 52.9% of the cohort. Feature importance analysis highlighted exercise-induced cough and recurrent bronchiolitis as the most significant predictors for asthma. Gradient Boosting demonstrated the highest diagnostic performance (F1: 0.974, ROC AUC: 0.997), followed closely by Random Forest (F1: 0.972, ROC AUC: 0.997) and AdaBoost (F1: 0.969, ROC AUC: 0.995). Logistic Regression, LDA, Decision Tree, and Naive Bayes showed moderate performance, while KNN had the lowest accuracy (F1: 0.566, ROC AUC: 0.615), indicating variable effectiveness among models. DISCUSSION: Machine learning algorithms show promise in improving diagnostic accuracy and efficiency in pediatric asthma, though further research is needed.
Pediatr+Allergy+Immunol 2025 ; 36 (12): e70258
