A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS

Elmehdi BENMALEK

elmehdi.benmalek@um5s.net.ma
E2SN, ENSAM de Rabat, Mohammed V University in Rabat (Morocco)

Jamal EL MHAMDI


E2SN, ENSAM de Rabat, Mohammed V University in Rabat (Morocco)

Abdelilah JILBAB


E2SN, ENSAM de Rabat, Mohammed V University in Rabat, (Morocco)

Atman JBARI


E2SN, ENSAM de Rabat, Mohammed V University in Rabat (Morocco)

Abstract

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 performed 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.


Keywords:

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

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Published
2022-12-19

Cited by

BENMALEK, E. ., EL MHAMDI, J., JILBAB, A. ., & JBARI, A. . (2022). A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS. Applied Computer Science, 18(4), 96–115. https://doi.org/10.35784/acs-2022-31

Authors

Elmehdi BENMALEK 
elmehdi.benmalek@um5s.net.ma
E2SN, ENSAM de Rabat, Mohammed V University in Rabat Morocco

Authors

Jamal EL MHAMDI 

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

Authors

Abdelilah JILBAB 

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

Authors

Atman JBARI 

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

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