A Machine Learning framework for Covid Detection Using Cough Sounds

The present COVID-19 diagnosis necessitates direct patient interaction, involves variable duration to get outcomes, and is costly. In certain poor nations, this is even unreachable to the populace at large, leading to a shortage of medical care. Therefore, a moderate, rapid, but also readily available method for the diagnosis of COVID-19 is essential. Several initiatives have been made to use smartphone-collected sounds and coughs to build machine learning algorithms that can categories and discriminate COVID-19 sounds with healthy tissue. The majority of prior studies used sounds like breathing or coughing to train their analyzers as well as get impressive outcomes. In order to carry out this significant investigation, we used this Coswara dataset, which contains recordings of nine distinct sound varieties of the COVID-19 state of cough, breathing, and speech. COVID-19 could be diagnosed more accurately using trained models on a variety of audio instead of a specific model trained on cough alone. This work examines the potential prospect of using machine learning techniques to enhance the identification of COVID-19 in such an initial and non-invasive manner through the monitoring of audio sounds. The XGBoost outperforms existing benchmark classification algorithms and achieves 92% accuracy with all sounds. Vowel/e/sound random forest with 98.36% was determined to be among the most effective, and the vowel/e/can also evaluated for the purpose of detecting compared to the other vowels; the impact of COVID-19 on sound quality is more precise.