## 4 Discussion
At first glance, this might limit the generalization of our findings; however, we see this as a strength in our exploratory study where the severity of the disease might in fact be helpful to amplify the features, using model building for cough classification. Factors such as practicality and feasibility made it favorable to conduct the study in a single hospital center. Future work should certainly include patients recruited from wider primary care settings to build a larger cough dataset that include other acute respiratory diseases. There are some limitations to our study. First, there were significantly more male participants in the normal-voluntary cohort. This was due to the predominance of male participants in the day surgery patient population, from which children in the normal-voluntary cohort were recruited. Nevertheless, we speculated that any sex bias was minimized through the mean age being below puberty when voice change occurs. Second, correct cough labeling by the attending physician was critical to provide the machine with our classification model. The physicians involved in the labeling of respiratory pathologies were not of the same clinical grade. Diagnoses of respiratory status in the Respiratory Ward and Clinic were made by at least a specialist consultant grade physician. Diagnoses of patients in the Emergency Department were made by specialists as well as residents. However, in our hospital, residents work under the supervision of specialist physicians. We assumed that the diagnosis was appropriate, as patients recruited from the Emergency Department were followed up and none required a re-visit to the hospital. Third, a fifth of our children in the asthmatic cohort had no wheeze on auscultation; we do not know if presence or absence of wheeze or other phenotypic expression might have affected the cough sounds. Last, we did not classify cough into acute or chronic cough, the duration of cough at presentation in children falls within acute cough definition, if it is less than 3 weeks in duration. In future, this study could be repeated in clinical settings with age stratification and greater granularity in terms of the phenotypic variation and duration of cough presented. It might also be interesting to correlate the acoustic findings to spirometry function, in future studies. As this is merely a pilot study, we have limited the machine learning to cough sounds arising from asthma and bronchial reactivity only, cough sounds from asthmatic children with concurrent respiratory conditions were excluded. Consequently, our classification model could not differentiate asthmatic coughs associated with concurrent respiratory conditions. We chose to work with GMM-UBM for a few reasons. First, this is a proof of concept pilot study to explore machine learning in cough sound classification, within a pediatric population. Most respiratory sounds investigated thus far were breathing sounds or lung sounds rather than cough sounds. Second, the GMM-UBM technique has proven its efficiency in respiratory sound classification [39; 40; 41; 42], and has been found to perform reliably, even with small data samples. While some recent studies showed that deep learning approaches such as Convolutional Neural Networks are more efficient for respiratory sound classification (notably lung sounds), these algorithms require a large dataset [43; 44; 45; 46; 47]. In [48], [PERSON] reported that for a smaller dataset, UBM-GMM outperforms deep, recurrent neural net models. Given the size of our dataset and the pilot nature of the study in children, we opted for the UBM-GMM. In future work, when our dataset grows larger, we will explore how deep learning approaches can further improve prediction accuracy.