Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990–2018)

Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens. Still, there are limited numbers of studies using clustering techniques to analyse the recent Global Dietary Database (GDD). This study aims to extend a...

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Veröffentlicht in:	Public health nutrition Jg. 28; H. 1; S. e89
Hauptverfasser:	Matousek, Adriano, Leung, Tiffany H, Pang, Herbert
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge, UK Cambridge University Press 21.04.2025
Schlagworte:	Adolescent Adult Aged Algorithms Beverages Cluster Analysis Clustering Clustering algorithm Databases, Factual Demographic variables Demographics Demography Diet - statistics & numerical data Diet - trends Diet Surveys Female Geography Global Dietary Database Global health Global Health - statistics & numerical data Humans Literature reviews Machine learning Male Middle Aged Multivariate Analysis Nutrient deficiency Nutrition Nutritional epidemiology Public health Research Paper Sugar Sugar-Sweetened Beverages - statistics & numerical data Time series Time-series clustering Trend Trends Variables Young Adult Trend Global Dietary Database Time-series clustering Clustering algorithm Machine learning
ISSN:	1368-9800, 1475-2727, 1475-2727
Online-Zugang:	Volltext
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