Lung Function Monitoring Using Machine Learning

Abstract

This project endeavours to develop a sophisticated system integrating machine learning algorithms aimed at continuously monitoring respiratory conduct. By harnessing the power of machine learning, the system aims to decipher pulmonary function tests, revolutionizing the management of respiratory diseases and potentially revolutionizing the diagnosis and treatment of various pulmonary and critical care conditions. Moreover, it innovatively utilizes machine learning to predict lung function based on acoustic signals from coughing and wheezing, facilitating noninvasive monitoring of asthma severity. The project additionally endeavours to forecast lung age and refine the existing dataset of audio samples to augment the accuracy and reliability of results. The utilization of machine learning in pulmonary function testing holds immense promise for the remote monitoring of high-risk patients and the early detection and treatment of lung diseases. By extending the abstract, we further elucidate the transformative potential of this initiative, underlining its significance in advancing healthcare by enabling remote monitoring, precise prognostication, and timely intervention in respiratory health.