- Tytuł:
- Efficient Covid-19 disease diagnosis based on cough signal processing and supervised machine learning
- Autorzy:
-
Bensid, Khaled
Lati, Abdelhai
Benlamoudi, Azeddine
Ghouar, Brahim Elkhalil
Senoussi, Mohammed Larbi - Powiązania:
- https://bibliotekanauki.pl/articles/2174478.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
voice disease
Covid-19
cough sounds
features extraction
classification
ekstrakcja cech
klasyfikacja
kaszel
przetwarzanie sygnału
uczenie maszynowe - Opis:
- The spread of the coronavirus has claimed the lives of millions worldwide, 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 accounts for 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), KNearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM.
- Źródło:
-
Diagnostyka; 2023, 24, 1; art. no. 2023103
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł