  Clinical characterization and interpretation of respiratory sound symptoms
have remained a challenge due to the similarities in the audio properties that
manifest during auscultation in medical diagnosis. The misinterpretation and
conflation of these sounds coupled with the comorbidity cases of the associated
ailments particularly, exercised-induced respiratory conditions; result in the
under-diagnosis and under-treatment of the conditions. Though several studies
have proposed computerized systems for objective classification and evaluation
of these sounds, most of the algorithms run on desktop and backend systems. In
this study, we leverage the improved computational and storage capabilities of
modern smartphones to distinguish the respiratory sound symptoms using machine
learning algorithms namely: Random Forest (RF), Support Vector Machine (SVM),
and k-Nearest Neighbour (k-NN). The appreciable performance of these
classifiers on a mobile phone shows smartphone as an alternate tool for
recognition and discrimination of respiratory symptoms in real-time scenarios.
Further, the objective clinical data provided by the machine learning process
could aid physicians in the screening and treatment of a patient during
ambulatory care where specialized medical devices may not be readily available.
