European Journal of Molecular & Clinical Medicine

Abstract

Humankind is witnessing a very unusual time confronting an unknown threat; the novel coronavirus. The coronavirus is a highly transmit table infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in Wuhan, China, and now fastly spreading among many developed and developing countries. This novel strain of coronavirus was named as Covid-19 by the World Health Organisation (WHO). Due to this continuing Covid-19 pandemic, the development of biometric and non-contactless devices is being helpful in reducing the spread of the coronavirus. By offering new insight, the demonstration of the adaptation of Machine Learning (ML) to the previous epidemic is helping researchers to tackle the latest Coronavirus outbreak. The main objective of this paper would be to use science to get the essence of covid and then to use technology to construct an approach to assess it. This discussion is further carried on to predict the individuals being infected by coronavirus using an integrated system. Later, to examine the reliability of the integrated framework, a comparative study will be carried out using machine learning algorithms. The integrated system will be trained using the training dataset that contains features such as body temperature measured using a biometric thermal scanner and pulse oxygen levels measured using an oximeter. The findings of the research would suggest that the models KNN, NB, and SVC performed better than the model's ANN and RF.