COVID and nutrition: A machine learning perspective

A self-report questionnaire survey was conducted online to collect big data from over 16000 Iranian families (who were the residents of 1000 urban and rural areas of Iran). The resulting data storage contained over 1 M records of data and over 1G records of automatically inferred information. Based...

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Detalles Bibliográficos
Autores principales: Jafari, Nafiseh, Besharati, Mohammad Reza, Izadi, Mohammad, Talebpour, Alireza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767975/
https://www.ncbi.nlm.nih.gov/pubmed/35071732
http://dx.doi.org/10.1016/j.imu.2022.100857
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author Jafari, Nafiseh
Besharati, Mohammad Reza
Izadi, Mohammad
Talebpour, Alireza
author_facet Jafari, Nafiseh
Besharati, Mohammad Reza
Izadi, Mohammad
Talebpour, Alireza
author_sort Jafari, Nafiseh
collection PubMed
description A self-report questionnaire survey was conducted online to collect big data from over 16000 Iranian families (who were the residents of 1000 urban and rural areas of Iran). The resulting data storage contained over 1 M records of data and over 1G records of automatically inferred information. Based on this data storage, a series of machine learning experiments was conducted to investigate the relationship between nutrition and the risk of contracting COVID-19. With highly accurate scores, the findings strongly suggest that foods and water sources containing certain natural bioactive and phytochemical agents may help to reduce the risk of apparent COVID-19 infection.
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spelling pubmed-87679752022-01-19 COVID and nutrition: A machine learning perspective Jafari, Nafiseh Besharati, Mohammad Reza Izadi, Mohammad Talebpour, Alireza Inform Med Unlocked Article A self-report questionnaire survey was conducted online to collect big data from over 16000 Iranian families (who were the residents of 1000 urban and rural areas of Iran). The resulting data storage contained over 1 M records of data and over 1G records of automatically inferred information. Based on this data storage, a series of machine learning experiments was conducted to investigate the relationship between nutrition and the risk of contracting COVID-19. With highly accurate scores, the findings strongly suggest that foods and water sources containing certain natural bioactive and phytochemical agents may help to reduce the risk of apparent COVID-19 infection. The Author(s). Published by Elsevier Ltd. 2022 2022-01-19 /pmc/articles/PMC8767975/ /pubmed/35071732 http://dx.doi.org/10.1016/j.imu.2022.100857 Text en © 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Jafari, Nafiseh
Besharati, Mohammad Reza
Izadi, Mohammad
Talebpour, Alireza
COVID and nutrition: A machine learning perspective
title COVID and nutrition: A machine learning perspective
title_full COVID and nutrition: A machine learning perspective
title_fullStr COVID and nutrition: A machine learning perspective
title_full_unstemmed COVID and nutrition: A machine learning perspective
title_short COVID and nutrition: A machine learning perspective
title_sort covid and nutrition: a machine learning perspective
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767975/
https://www.ncbi.nlm.nih.gov/pubmed/35071732
http://dx.doi.org/10.1016/j.imu.2022.100857
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