ArticleMaterials today. Proceedings2022
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.
Article in Materials today. Proceedings, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Study of Text Patterns Found on Social Networks of Mental Health Reactions to COVID-19.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023Article
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Authors and funding
3 authors.
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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%.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.