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

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dc.contributor.authorGuzman Vilca, Wilmer Cristobal
dc.contributor.authorCarrillo Larco, Rodrigo M.
dc.contributor.authorCastillo Cara, José Manuel
dc.date.accessioned2024-05-20T11:35:12Z
dc.date.available2024-05-20T11:35:12Z
dc.date.issued2022-01-25
dc.description.abstractGlobal 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.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.7554/eLife.72930
dc.identifier.issn2050-084X
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12264
dc.journal.titleeLife
dc.journal.volume11
dc.language.isoen
dc.publishereLife Sciences Publications
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleDevelopment, validation, and application of a machine learning model to estimate salt consumption in 54 countrieses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationc0e39bd2-c0d8-4743-953d-488baf6b977e
relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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