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Tytuł artykułu

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

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Identyfikatory
Warianty tytułu
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
Rocznik
Strony
96--115
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
  • E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco
  • E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco
  • E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco
autor
  • E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco
Bibliografia
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  • [13] Han, J., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2021). Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data. In ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8328–8332). IEEE.
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  • [18] Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., Cao, K., Liu, D., Wang, G., Xu, Q., Fang, X., Zhang, S., Xia, J., & Xia, J. (2020). Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology, 296(2), E65–E71. https://doi.org/10.1148/radiol.2020200905
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6d09a340-d41c-4968-b821-62a379ebd135
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