Non-invasive prediction mechanism for COVID-19 disease using machine learning algorithms

Arnav Bhardwaj, Hitesh Agarwal, Anuj Rani, Prakash Srivastava, Manoj Kumar and Sunil Gupta

International Journal of Critical Infrastructures, 2024, vol. 20, issue 2, 111-124

Abstract: This paper has focused on developing a model to detect non-diagnostically whether the person is infected with the COVID-19 disease using all relevant symptoms and details mentioned by the person and then comparing it with a pre-defined dataset of positive cases using machine learning. Different models have been developed to predict the same but none of them focused on the detection of COVID-19 based on symptoms. In a developing nation with a huge population, where the diagnostic availability is scarce, just scanning the body temperature will not help in detection of COVID-19 of a particular individual. This paper presents a model that can predict COVID-19 cases without any testing kit to an accuracy of 99.30%, performing better than other similar approaches with objective to put forward a method that can reduce the need of producing testing kits and also the need to wait for hours before we get the results.

Keywords: COVID-19; non-invasive; symptoms; machine learning. (search for similar items in EconPapers)
Date: 2024
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