Early Detection of Covid-19 Through Cough Sound Recognition using LPC and K-NN algorithm

ABSTRACT

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by the SARS-CoV-2 virus. This disease has spread worldwide since the beginning of 2020. Patients with this highly contagious disease generally experience only mild to moderate respiratory problems such as sore throat, cough, runny nose, fever, shortness of breath, and fatigue. However, some will become seriously ill and may cause severe respiratory distress or in severe cases multiple organ failure. Therefore, early identification of COVID-19 patients is very important. In this study, a disease detection system was created using an open dataset from COUGHVID which were contained the coughing sound of the Covid-19 disease. The implementation of the cough voice recognition system uses the K-Nearest Neighbor (K-NN) machine learning method and the Linear Predictive Coding (LPC) as method of extracting features from voice. The system was built using the Raspberry Pi 3 b+ microcontroller with microphone voice input and connected to a 3.5-inch LCD touchscreen display as the interface of the system device. The test uses a coughing sound as input through a microphone and processed by LPC feature extraction. At each running process, about 399 MB of memory is used from a total of 1 GB of memory. Meanwhile, the prediction of coughing sounds with the K-NN classification algorithm using 5 neighbors produces accuracy of 62% to predict disease. © 2022 ACM.