A Machine Learning Approach for Delineating Similar Sound Symptoms of Respiratory Conditions on a Smartphone

TL;DR

This paper presents a machine learning approach implemented on smartphones to accurately distinguish similar respiratory sound symptoms, aiding real-time diagnosis and potentially improving clinical outcomes in resource-limited settings.

Contribution

It demonstrates the feasibility of using mobile devices with machine learning algorithms for real-time respiratory sound classification, a novel application compared to traditional desktop-based systems.

Findings

Support Vector Machine achieved high accuracy in classification.

Smartphone-based system performs comparably to desktop systems.

Real-time respiratory sound discrimination is feasible on mobile devices.

Abstract

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.