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Covid Wave Prediction using SARIMA Machine Learning Algorithm
2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2023 ; 2023.
Artigo em Inglês | Scopus | ID: covidwho-2303153
ABSTRACT
A speedy and accurate diagnosis of COVID-19 is made possible by effective SARS-Co V -2 screening, which can also lessen the strain on health care systems. There have been built prediction models that assess the likelihood of infection by combining a number of parameters. These are intended to help medical professionals worldwide prioritize patients, particularly when there are few healthcare resources available. From a dataset of 51,831 tested people, out of which 4,769 were confirmed to have COVID-19 virus, a machine learning method was developed and trained. Records of the following week with 47,401 tested people, of which 3,624 were tested positive was also considered. Our method accurately predicted the COVID-19 test results using eight binary characteristics, including gender, age 60, known contact with an infected person, and the presence of five early clinical signs. © 2023 IEEE.
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Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: Scopus Tipo de estudo: Estudo prognóstico Idioma: Inglês Revista: 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2023 Ano de publicação: 2023 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: Scopus Tipo de estudo: Estudo prognóstico Idioma: Inglês Revista: 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2023 Ano de publicação: 2023 Tipo de documento: Artigo