An ensemble learning approach to digital corona virus preliminary screening from cough sounds

Abstract This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound da...

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Autores principales:	Emad A. Mohammed, Mohammad Keyhani, Amir Sanati-Nezhad, S. Hossein Hejazi, Behrouz H. Far
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a8c423bfccc843dab31a04987be2c16c
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spelling	oai:doaj.org-article:a8c423bfccc843dab31a04987be2c16c2021-12-02T16:06:44ZAn ensemble learning approach to digital corona virus preliminary screening from cough sounds10.1038/s41598-021-95042-22045-2322https://doaj.org/article/a8c423bfccc843dab31a04987be2c16c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95042-2https://doaj.org/toc/2045-2322Abstract This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.Emad A. MohammedMohammad KeyhaniAmir Sanati-NezhadS. Hossein HejaziBehrouz H. FarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Emad A. Mohammed Mohammad Keyhani Amir Sanati-Nezhad S. Hossein Hejazi Behrouz H. Far An ensemble learning approach to digital corona virus preliminary screening from cough sounds
description	Abstract This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.
format	article
author	Emad A. Mohammed Mohammad Keyhani Amir Sanati-Nezhad S. Hossein Hejazi Behrouz H. Far
author_facet	Emad A. Mohammed Mohammad Keyhani Amir Sanati-Nezhad S. Hossein Hejazi Behrouz H. Far
author_sort	Emad A. Mohammed
title	An ensemble learning approach to digital corona virus preliminary screening from cough sounds
title_short	An ensemble learning approach to digital corona virus preliminary screening from cough sounds
title_full	An ensemble learning approach to digital corona virus preliminary screening from cough sounds
title_fullStr	An ensemble learning approach to digital corona virus preliminary screening from cough sounds
title_full_unstemmed	An ensemble learning approach to digital corona virus preliminary screening from cough sounds
title_sort	ensemble learning approach to digital corona virus preliminary screening from cough sounds
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a8c423bfccc843dab31a04987be2c16c
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An ensemble learning approach to digital corona virus preliminary screening from cough sounds

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