Machine Learning based COVID-19 Cough Classification Models - A Comparative Analysis
| Autor: | Jayavrinda Vrindavanam, Hari Haran Shankar, Gaurav Nagesh, Raghunandan Srinath |
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| Rok vydání: | 2021 |
| Předmět: |
Health professionals
Coronavirus disease 2019 (COVID-19) business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Statistical classification Identification (information) Ranking 020204 information systems Healthy individuals 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) |
| Zdroj: | 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). |
| DOI: | 10.1109/iccmc51019.2021.9418358 |
| Popis: | COVID-19 continues to be a global pandemic and many a technological intervention are already in place for identification of COVID-19 patients. The paper focuses on the contactless detection of COVID-19 patients by analyzing their respective cough audio samples. The paper demonstrates three machine learning classification models and determines the better classifier among these three models. The model has made use of 15 dominant features. The paper has employed a method of selecting features based on ranking different scores derived from the feature selecting algorithms. The initial results will be forming part of a larger project of developing suitable interfaces, as such devices can reduce the stress on frontline workers and provide an efficient way to manage the resources and time of healthcare professionals. The proposed method has been tested on cough audios both COVID-19 positive and healthy individuals, and the results are promising. |
| Databáze: | OpenAIRE |
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