COVID and nutrition: A machine learning perspective

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

doi:10.1016/j.imu.2022.100857

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Literature [33][34][35] and highlighted the strong efect of vitamins on obesity, vascular dementia, and Alzheimer's disease through various machine learning algorithms. Essential conclusions that selective nutritional intake efectively reduces COVID-19 infection and mortality have also been drawn using multiple regression models and classifcation algorithms [36,37].…”

Section: Related Workmentioning

confidence: 99%

m-Health of Nutrition: Improving Nutrition Services with Smartphone and Machine Learning

Liu

Jiang

et al. 2023

Mobile Information Systems

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Balanced and adequate nutrient intakes are increasingly desired for people, especially those affected by chronic diseases. How to help people to realize appropriate nutrient consumption is an issue that should be addressed in smart healthcare. This study proposes a novel smartphone-based platform that includes dietary records, nutrition data analysis, and online nutritional guidance to improve remote nutrition services. Positive results from a trial conducted in cooperation with a hospital confirm the platform’s promising capabilities in nutrition guidance and disease management. Additionally, to explore how nutrient intake impacts human health, this study took the relationship between hypertension and nutrient intake, and personal information as a case study. The findings indicate that our platform can provide detailed nutrient analysis services, and machine-learning-based prediction methods can accurately predict the user’s blood pressure with little error.

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