# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 5 Conclusions  
COVID-19 affects the human respiratory system. Coughing is an audio signal that comes out of the body only after going through the respiratory system. Therefore, if the COVID-19 virus has affected the respiratory system, it will inevitably affect the audio signal generated. For this reason, COVID-19 diagnostic studies using coughing sound are being actively conducted.  
In this study, we used only crowdsourced cough sound data to distinguish between the cough sound of COVID-19 positive people and that of healthy people. A new feature set based on features identified in previous studies was extracted to improve the performance of the analysis of cough sounds. If COVID-19 diagnosis is possible with only the cough sound, it will be possible to receive a faster diagnosis for prescreening purposes before undergoing the RT-PCR test. Depending on the diagnosis results, the user will be able to go to the hospital for an accurate diagnosis and minimize contact with the outside world. This would be able to help prevent infection of COVID-19.  
We have created a model to diagnose the COVID-19 condition from cough sounds; however, before the implementation of machine learning algorithms, reliable data are needed for the generalization of the model [27]. Therefore, in order to ensure high performance in all locations and situations, ML solutions must be trained and tested with collected data from various people in various locations. Future research aims to find high-quality data collected from more diverse locations to compensate for this point. In addition, due to the inevitable lack of COVID-19-positive cough sound data, we will study the best way to combine the databases and how to combine models to compensate for this. Furthermore, the main challenge of clinical COVID-19 diagnosis is that the symptoms are similar to those of other common respiratory, lung and heart diseases [28]. Therefore, models should be tested to distinguish COVID-19 from other diseases, such as non-COVID-19 pneumonia, respiratory infections, asthma, and chronic lung disease exacerbations [29; 30]. Therefore, we will conduct additional sub-analysis tests and conduct research on effective audio features. Using this approach, we hope to develop faster and larger-scale diagnostic technology that can be used by anyone with a smartphone or computer. In the future, it is expected that a technology for diagnosing other respiratory diseases using audio signals will be studied.  
Figure 8: The ROC curve for each experiment.  
Conceptualization, M.-J.S.; methodology, M.-J.S. and S.-P.L.; investigation, M.-J.S.; writing--original draft preparation, M.-J.S.; writing--review and editing, S.-P.L.; project administration, S.-P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Sangmyung University.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Experiments used publicly available datasets.  
This work was supported by the 2021 Research Grant from Sangmyung University.  
The authors declare no conflict of interest.