Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning
| 1 | Faculty of New information and communication technologies
University Kasdi Merbah Ouargla , BP 511, 30000, Ouargla, Algeria |
| 2 | Faculty of New information and communication technologies
University Kasdi Merbah Ouargla, BP 511, 30000, Ouargla, Algeria |
CORRESPONDING AUTHOR
Abdelhai Lati
Faculty of New information and communication technologies University Kasdi Merbah Ouargla , BP 511, 30000, Ouargla, Algeria
Faculty of New information and communication technologies University Kasdi Merbah Ouargla , BP 511, 30000, Ouargla, Algeria
Submission date: 2022-08-07
Final revision date: 2022-11-03
Acceptance date: 2022-11-21
Online publication date: 2022-12-01
Publication date: 2023-01-02
Diagnostyka 2023;24(1):2023103
KEYWORDS
TOPICS
ABSTRACT
The spread of the coronavirus has claimed the lives of millions around the world, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing amounts to 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation. Second, cough signal extraction; and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), K - Nearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female cough was 99.6% using KNN and 88% for males using SVM
REFERENCES (24)
1.
Pramono RX, Imtiaz SA, Rodriguez-Villegas E. Automatic cough detection in acoustic signal using spectral features. In2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019:7153-7156. https://doi.org/10.1109/EMBC.2....
2.
Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NM, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM. Cough sound detection and diagnosis using artificial intelligence techniques: challenges and opportunities. Ieee Access. 2021 15; 9:102327-44. https://doi.org/10.1109/ACCESS....
3.
World Health Organization. Coronavirus disease 2019 (covid-19): situation report. 2020;51.
4.
Sharma N, Krishnan P, Kumar R, Ramoji S, Chetupalli SR, Ghosh PK, Ganapathy S. Coswara–a database of breathing, cough, and voice sounds for COVID-19 Diagnosis. arXiv 2020; arXiv:2005.10548. https://doi.org/10.48550/arXiv....
5.
Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo, C. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual. 2021:3474–3484. https://doi.org/10.48550/arXiv.... 05919.
6.
Despotovic V, Ismael M, Cornil M, Mc Call R, Fagherazzi G. Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results. Comput. Biol. Med. 2021;138: 104944. https://doi.org/10.1016/j.comp....
7.
Islam R, Abdel-Raheem E, Tarique M. A study of using cough sounds and deep neural networks for the early detection of COVID-19. Biomed. Eng. Adv. 2022;3:100025. https://doi.org/10.1016/j.bea.....
8.
Rahman T, Ibtehaz N, Khandakar A, Hossain MSA, Mekki YMS, Ezeddin M, Bhuiyan EH, Ayari MA, Tahir A, Qiblawey Y. An intelligent application to detect COVID-19 patients using cough and breath sounds. Diagnostics. 2022; 12:920. https://doi.org/10.3390/diagno....
9.
Morice AH, Fontana GA, Belvisi MG, et al. ERS guidelines on the assessment of cough, European Respiratory Journal. 2007;29(6):1256–1276. https://doi.org/10.1183/090319....
10.
Islam Rumana, Abdel-Raheem Esam, Tarique Mohammed. Early detection of COVID-19 patients using chromagram features of cough sound recordings with machine learning algorithms. In: 2021 International Conference on Microelectronics (ICM). IEEE. 2021:82-85. https://doi.org/10.1109/ICM526....
11.
Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intelligent Systems and their applications. 1998;13(4):18-28. https://doi.org/10.1109/5254.7....
12.
Huynh TH, Tran VA, Tran HD. Semi-supervised tree support vector machine for online cough recognition. In Twelfth Annual Conference of the International Speech Communication Association 2011.
13.
Hadjaidji E, Korba M, Khelil K. Spasmodic dysphonia detection using machine learning classifiers. In Inter Conference on Recent Advances in Mathematics and Infor (ICRAMI). 2021;1–5. https://doi.org/10.1109/ICRAMI....
14.
Rajani Kumari LV, Padma Sai Y. Classification of arrhythmia beats using optimized K-nearest neighbor classifier. InIntelligent Systems. 2021:349-359. https://doi.org/10.1007/978-98....
15.
Brian Kulis. Metric Learning: A Survey. Foundations and Trends® in Machine Learning: 2013;5(4):287-364. https://doi.org/10.1561/220000....
16.
Khorshid SF, Abdulazeez AM. Breast cancer diagnosis based on k-nearest neighbors: A review. Journal of Archaeology of Egypt/Egyptology. 2021;15;18(4):1927-51. https://archives.palarch.nl/in....
17.
Sipper M, Moore JH. Conservation machine learning: a case study of random forests. Scientific Reports. 2021;11;11(1):1-6. https://doi.org/10.1038/s41598....
18.
Zhang, Z. Introduction to machine learning: k-nearest neighbors. Annals of translational medicine. 2016;4(11). https://doi.org/10.21037/atm.2....
19.
Usman M, Zubair M, Ahmad Z, Zaidi M, Ijyas T, Parayangat M, Wajid M, Shiblee M, Ali SJ. Heart rate detection and classification from speech spectral features using machine learning. Archives of Acoustics, 2021;46. https://doi.org/10.24425/aoa.2....
20.
Goswami S, Pramanick R, Patra A, Rath SP, Foltin M, Ariando A, Thompson D, Venkatesan T, Goswami S, Williams RS. Decision trees within a molecular memristor. Nature. 2021;597(7874):51-6. https://doi.org/10.1038/s41586....
21.
Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends. 2021;24;2(01):20-28. https://doi.org/10.38094/jastt....
22.
Bilski P, Bobiński P, Krajewski A, Witomski P. Detection of wood boring insects’ larvae based on the acoustic signal analysis and the artificial intelligence algorithm. Archives of Acoustics. 2016;42(1):61-70. https://doi.org/10.1515/aoa-20....
23.
Zhiyong G, Jiwu L, Rongxi W. Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model. Eksploatacja i Niezawodność. 2021;23(1). https://doi.org/10.17531/ein.2....
24.
Pizzo DT, Esteban S, Scetta M. IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples. arXiv preprint arXiv: 2021;2104:13247. https://doi.org/10.48550/arXiv....
„Podniesienie poziomu naukowego i poziomu umiędzynarodowienia czasopisma Diagnostyka oraz upowszechniania informacji o wynikach badań lub prac rozwojowych poprzez udział uznanych zagranicznych recenzentów w ocenie publikacji - zadanie finansowane w ramach umowy 745/P-DUN/2017. ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę”
© 2006-2023 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.