A cough-based COVID-19 detection system using PCA and machine learning classifiers

• Autorzy: Benmalek Elmehdi , Mhamdi Jamal El , Jilbab Abdelilah , Jbari Atman

Identyfikatory

DOI

10.35784/acs-2022-31

• Języki publikacji: EN

Abstrakty

EN

In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we per-formed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients.

Słowa kluczowe

EN

COVID-19; cough recordings; machine learning; PCA; classification

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 4
• Strony: 96--115
• Opis fizyczny: Bibliogr. 30 poz., fig., tab.

Twórcy

autor

Benmalek Elmehdi

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0003-1078-1421

autor

Mhamdi Jamal El

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

autor

Jilbab Abdelilah

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0002-1577-9040

autor

Jbari Atman

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0002-1855-2503

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