Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19
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 countr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/8/4/371 |
| _version_ | 1797416655382380544 |
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| author | María Teresa García-Ordás Natalia Arias Carmen Benavides Oscar García-Olalla José Alberto Benítez-Andrades |
| author_facet | María Teresa García-Ordás Natalia Arias Carmen Benavides Oscar García-Olalla José Alberto Benítez-Andrades |
| author_sort | María Teresa García-Ordás |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:06:22Z |
| format | Article |
| id | doaj.art-7593522777934862b73afa5c70454e9e |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T06:06:22Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-7593522777934862b73afa5c70454e9e2023-12-03T12:02:57ZengMDPI AGHealthcare2227-90322020-09-018437110.3390/healthcare8040371Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19María Teresa García-Ordás0Natalia Arias1Carmen Benavides2Oscar García-Olalla3José Alberto Benítez-Andrades4SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, C.P., 24071 León, SpainSALBIS Research Group, Department of Nursing and Physiotherapy Health Science School, University of León, Avenida Astorga s/n, Ponferrada, 24401 León, SpainSALBIS Research Group, Department of Electric, Systems and Automatics Engineering, University of León, Campus of Vegazana s/n, León, 24071 León, SpainArtificial Intelligence Department, Xeridia S.L., Av. Padre Isla 16, 24002 León, SpainSALBIS Research Group, Department of Electric, Systems and Automatics Engineering, University of León, Campus of Vegazana s/n, León, 24071 León, SpainCOVID-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.https://www.mdpi.com/2227-9032/8/4/371COVID-19countriesfatproteinKCaldeaths |
| spellingShingle | María Teresa García-Ordás Natalia Arias Carmen Benavides Oscar García-Olalla José Alberto Benítez-Andrades Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 Healthcare COVID-19 countries fat protein KCal deaths |
| title | Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 |
| title_full | Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 |
| title_fullStr | Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 |
| title_full_unstemmed | Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 |
| title_short | Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19 |
| title_sort | evaluation of country dietary habits using machine learning techniques in relation to deaths from covid 19 |
| topic | COVID-19 countries fat protein KCal deaths |
| url | https://www.mdpi.com/2227-9032/8/4/371 |
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