Bibliographic Details
| Title: |
Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 |
| Authors: |
García-Ordás, María Teresa, Arias, Natalia, Benavides, Carmen, García-Olalla, Oscar, Benítez-Andrades, José Alberto |
| Source: |
Healthcare 2020, 8(4), 371 |
| Publication Year: |
2024 |
| Collection: |
Computer Science
Quantitative Biology |
| Subject Terms: |
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods |
| More Details: |
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. |
| Document Type: |
Working Paper |
| DOI: |
10.3390/healthcare8040371 |
| Access URL: |
http://arxiv.org/abs/2402.12558 |
| Accession Number: |
edsarx.2402.12558 |
| Database: |
arXiv |
