Non-invasive prediction mechanism for COVID-19 disease using machine learning algorithms Online publication date: Mon, 18-Mar-2024
by Arnav Bhardwaj; Hitesh Agarwal; Anuj Rani; Prakash Srivastava; Manoj Kumar; Sunil Gupta
International Journal of Critical Infrastructures (IJCIS), Vol. 20, No. 2, 2024
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.
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