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COVID and nutrition: A machine learning perspective

Jafari, N ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.imu.2022.100857
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. 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. © 2022 The Author(s)
  6. Keywords:
  7. Big data ; COVID-19 ; Diet ; Machine learning ; Multilayer perceptron ; Nutrition ; Random forest ; Phytochemical ; Coronavirus disease 2019 ; Food intake ; Health survey ; Human ; Infection risk ; Information storage ; Iran ; Lifestyle ; Major clinical study ; Mortality rate ; Nutritional assessment ; Observational study ; Questionnaire ; Risk reduction ; Rural area ; Self report ; Tea consumption ; Urban area ; Water supply
  8. Source: Informatics in Medicine Unlocked ; Volume 28 , 2022 ; 23529148 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S2352914822000119