Abstract: Respiratory illnesses are among the most prevalent child health conditions globally, with the potential to cause considerable morbidity and mortality if not promptly diagnosed and treated. Proper and timely classification of respiratory conditions like asthma, bronchiolitis, pneumonia, and upper respiratory infections is essential to provide proper treatment and avoid complications. This research investigates the creation of a pediatric patient-specific respiratory disease classification system based on clinical signs, auscultation results, and diagnostic imaging information. Taking advantage of machine learning methods, such as decision trees, support vector machines, and deep learning, we seek to enhance the accuracy of diagnosis and facilitate clinical decisions within pediatric healthcare environments. We have used annotated pediatric clinic medical records, considering pediatric patients aged between 6 and 14 years. Initial findings show exceptional classification accuracy, particularly in demarcation between viral and bacterial infections. This research highlights the ability of data-driven methods in promoting pediatric respiratory management and provides the groundwork for putting intelligent diagnostic tools to clinical use.
Keywords: Respiratory sound classification, Adventitious respiratory sounds, Respiratory diseases, Deep learning.
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DOI:
10.17148/IJARCCE.2025.145115
