Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma

Traditional auscultation, heavily dependent on the subjective judgment of physicians, can lead to variability in diagnoses. This study aimed to explore the application of machine learning algorithms for analyzing breath sounds in children with asthma, particularly those with asthma in remission and those with cough variant asthma (CVA). Our study collected breath sound data from 50 children with asthma (30 with asthma in remission and 20 with CVA). First, we preprocessed and extracted the breath sound data. Second, machine learning techniques were applied to objectively classify and evaluate the breath sounds of pediatric asthma patients. Then logistic regression, random forest, and support vector machine algorithms were employed to train models and predict outcomes. In this study, the support vector machine achieved the best performance in distinguishing between breath sounds from children with asthma in remission and those with CVA. It reached an accuracy of 98.32%, a sensitivity of 96.23%, and an area under the receiver operating characteristic curve of 0.99 in predicting pediatric asthma subtypes. Our findings highlight the potential of machine learning models to revolutionize the diagnosis and treatment of pediatric asthma, offering a pathway towards more precise and individualized therapeutic strategies. ClinicalTrials.gov number: ChiCTR2300077717 (Registration Date: 2023-11-16).