Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.

Autor:	Askari Nasab K; Materials Science and Engineering Department, Sharif University of Technology, Tehran, Iran., Mirzaei J; Infectious Disease Research Center, Department of Infectious Diseases, Aja University of Medical Sciences, Tehran, Iran.; Infectious Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Zali A; Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.; USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Gholizadeh S; Civil Engineering Department, Tehran University of Technology, Tehran, Iran., Akhlaghdoust M; Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.; USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2023 Feb 15; Vol. 6, pp. 1100112. Date of Electronic Publication: 2023 Feb 15 (Print Publication: 2023).
DOI:	10.3389/frai.2023.1100112
Abstrakt:	Introduction: The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. Method: In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. Result: With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. Conclusion: These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%. 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 © 2023 Askari Nasab, Mirzaei, Zali, Gholizadeh and Akhlaghdoust.)
Databáze:	MEDLINE
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