Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach.

ABSTRACT:

Introduction

To self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients.

Methods

A threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung functi

SUBMITTER: Alam MZ 

PROVIDER: S-EPMC8861188 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature