COVID and nutrition: A machine learning perspective

Nafiseh Jafari; Mohammad Reza Besharati; Mohammad Izadi; Alireza Talebpour

doi:10.1016/j.imu.2022.100857

COVID and nutrition: A machine learning perspective

Inform Med Unlocked. 2022:28:100857. doi: 10.1016/j.imu.2022.100857. Epub 2022 Jan 19.

Authors

Nafiseh Jafari¹, Mohammad Reza Besharati², Mohammad Izadi², Alireza Talebpour³

Affiliations

¹ Engineering Department, University of Qom, Qom, Iran.
² Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
³ Computer Science and Engineering Department, Shahid Beheshti University, Tehran, Iran.

Abstract

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.

Keywords: Big data; COVID-19; Diet; Machine learning; Multilayer perceptron; Nutrition; Random forest.