Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries

Guzman Vilca, Wilmer Cristobal, Castillo Cara, Manuel y Carrillo Larco, Rodrigo M . (2022) Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries. eLife

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Título Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries
Autor(es) Guzman Vilca, Wilmer Cristobal
Castillo Cara, Manuel
Carrillo Larco, Rodrigo M
Materia(s) Informática
Abstract Global targets to reduce salt intake have been proposed, but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption at the population level based on simple predictors and applied this model to national surveys in 54 countries. We used 21 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on sex, age, weight, height, and systolic and diastolic blood pressure. We applied the ML model to 54 new surveys to quantify the mean salt consumption in the population. The pooled dataset in which we developed the ML model included 49,776 people. Overall, there were no substantial differences between the observed and ML-predicted mean salt intake (p<0.001). The pooled dataset where we applied the ML model included 166,677 people; the predicted mean salt consumption ranged from 6.8 g/day (95% CI: 6.8–6.8 g/day) in Eritrea to 10.0 g/day (95% CI: 9.9–10.0 g/day) in American Samoa. The countries with the highest predicted mean salt intake were in the Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence. An ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available.
Editor(es) eLife Sciences Publications
Fecha 2022-01-25
Formato application/pdf
Identificador bibliuned:557-Jmcastillo-0007
http://e-spacio.uned.es/fez/view/bibliuned:557-Jmcastillo-0007
DOI - identifier https://doi.org/10.7554/eLife.72930
ISSN - identifier 2050-084X
Nombre de la revista eLife
Número de Volumen 11
Publicado en la Revista eLife
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en eLife (2022) 11, está disponible en línea en el sitio web del editor: eLife Sciences Publications https://doi.org/10.7554/eLife.72930
Notas adicionales The copyrighted version of this article, first published in eLife (2022) 11, is available online at the publisher's website: eLife Sciences Publications https://doi.org/10.7554/eLife.72930

 
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Creado: Wed, 28 Feb 2024, 20:11:43 CET