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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7544| Real-time Estimation of Global CFR Ascribed to COVID-19 Confirmed Cases Applying Machine Learning Technique | |
| MONALISHA PATTNAIK Aryan Pattnaik | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| https://doi.org/10.1101/2021.12.27.21268463 | |
| https://www.medrxiv.org/content/10.1101/2021.12.27.21268463v1 | |
| The COVID-19 is declared as a public health emergency of global concern by World Health Organisation (WHO) affecting a total of 201 countries across the globe during the period December 2019 to January 2021. As of January 25, 2021, it has caused a pandemic outbreak with more than 99 million confirmed cases and more than 2 million deaths worldwide. The crisp of this paper is to estimate the global risk in terms of CFR of the COVID-19 pandemic for seventy deeply affected countries. An optimal regression tree algorithm under machine learning technique is applied which identified four significant features like diabetes prevalence, total number of deaths in thousands, total number of confirmed cases in thousands, and hospital beds per 1000 out of fifteen input features. This real-time estimation will provide deep insights into the early detection of CFR for the countries under study. | |
| medRxiv and bioRxiv | |
| 29-12-2021 | |
| Preimpreso | |
| www.medrxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Real time estimation of Global CFR Ascribed to COVID 19 Confirmed Cases Applying Machine Learning Technique.pdf | 172.04 kB | Adobe PDF | Visualizar/Abrir |