Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning

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

Keywords

• covid-19
• classification
• voice disease
• cough sounds
• features extraction