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

Autor:	Alam MZ; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.; Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Simonetti A; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.; Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy., Brillantino R; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.; Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy., Tayler N; Peter Doherty Institute, The University of Melbourne, Melbourne, VIC, Australia., Grainge C; Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.; Department of Respiratory Medicine, John Hunter Hospital, Newcastle, NSW, Australia., Siribaddana P; Postgraduate Institute of Medicine, University of Colombo, Colombo, Sri Lanka., Nouraei SAR; Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom.; Robert White Centre for Airway Voice and Swallowing, Poole Hospital, Poole, United Kingdom., Batchelor J; Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom., Rahman MS; Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Mancuzo EV; Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Holloway JW; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.; National Institute for Health Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom., Holloway JA; Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.; MSc Allergy, Faculty of Medicine, University of Southampton, Southampton, United Kingdom., Rezwan FI; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.; Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2022 Feb 08; Vol. 4, pp. 750226. Date of Electronic Publication: 2022 Feb 08 (Print Publication: 2022).
DOI:	10.3389/fdgth.2022.750226
Abstrakt:	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 function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples. Results: The RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%). Conclusion: Our machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Alam, Simonetti, Brillantino, Tayler, Grainge, Siribaddana, Nouraei, Batchelor, Rahman, Mancuzo, Holloway, Holloway and Rezwan.)
Databáze:	MEDLINE
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