Abstract
The goal of this research is to see how well is a fast primary screening method for COVID-19 that relies only on cough sounds collected from 2200 clinically verified samples utilizing the laboratory molecular testing performs (1100 Covid-19 positive and 1100 Covid-19 negative). The clinical labels were applied to the results, and severity of the samples may be judged based on quantitative RT-PCR (qRT-PCR), cycle threshold, and patient lymphocyte counts. The fast spread of the COVID-19 virus poses a significant danger of serious pulmonary disease, and it also causes the most heinous harm to humanity. As a result, a quick and clear disease classification model to distinguish between normal and COVID-19 infected individuals is critical. In this article, we describe the various machine learning and other models that have been used to predict COVID-19 patients.
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Singh, H. et al. (2023). Effective Overview of Different ML Models Used for Prediction of COVID-19 Patients. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-19-0151-5_15
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