Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy. (2022)

• Record Type: Journal Article
• Authors: Deepa, N.; Sathya Priya, J.; Devi, T.
• Is Part Of: Materials today. Volume 62:Part 7(2022)
• Page Start: 4795
• Page End: 4799
• Publication Date: 2022
• Subjects: Accuracy -- COVID-19 -- Naive Bayes classifier -- Prediction -- Random forest; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.03.345
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Infections such as COVID-19 are affecting the entire world and measures such as social distancing can be done so that the contact among people is reduced. IoT devices usage keeps on increasing every day thereby connecting the environments physically. Among the current technologies, machine learning can be employed along with IoT devices. Predicting the risk related with COVID-19, a novel method employing machine learning is proposed. Random forest and Naive Bayes classifier are used for the prediction from the data collected with the help of sensors. Groups of people are recognized and the disease impact can be reduced for the particular group with more population. The accuracy of RF is 97% and for NB it is 99%.