ArticleHealthcare (Basel, Switzerland)2020
Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19.
Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Factors influencing urban Ghanaian consumers' preferences for meals/products from multinational food corporations and gender subgroups: a supervised machine learning MaxDiff designs study.Frontiers in nutrition · 2026Article
- COVID-19 mortality and nutrition through predictive modeling and optimization based on grid search.Scientific reports · 2025Article
- Coronavirus disease (COVID-19) cases analysis using machine-learning applications.Applied nanoscience · 2023Article
- Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction.Expert systems with applications · 2022Article
- COVID and nutrition: A machine learning perspective.Informatics in medicine unlocked · 2022Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- HANA: A Healthy Artificial Nutrition Analysis model during COVID-19 pandemic.Computers in biology and medicine · 2021Article
- Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov.International journal of environmental research and public health · 2021Review
- Mediterranean Diet for the Prevention of Gestational Diabetes in the Covid-19 Era: Implications of Il-6 In Diabesity.International journal of molecular sciences · 2021Review
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Authors and funding
5 authors.
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Abstract
COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.
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