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dc.contributor.authorGuzman-Vilca, Wilmer Cristobal
dc.contributor.authorCastillo Cara, José Manuel
dc.contributor.authorCarrillo-Larco, Rodrigo M.
dc.contributor.otherCastillo Cara, José Manuel
dc.date.accessioned2023-03-16T13:54:28Z
dc.date.available2023-03-16T13:54:28Z
dc.date.issued2022
dc.identifier.citationGuzman-Vilca, W. C., Castillo-Cara, J. M. & Carrillo-Larco, R. M. (2022). Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries. eLife, 11. https://doi.org/10.7554/eLife.72930es_PE
dc.identifier.issn2050-084X
dc.identifier.urihttps://hdl.handle.net/20.500.12724/17919
dc.descriptionIndexado en Scopuses_PE
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.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publishereLife Sciences Publications Ltdes_PE
dc.relation.ispartofurn:issn: 2050-084X
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectSales_PE
dc.subjectConsumoes_PE
dc.subjectAprendizaje automáticoes_PE
dc.subjectSalten_EN
dc.subjectConsumptionen_EN
dc.subjectMachine learningen_EN
dc.subject.classificationMarketing / Comportamiento de compra, consumo o uso de productos y servicioses_PE
dc.titleDevelopment, validation, and application of a machine learning model to estimate salt consumption in 54 countrieses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.type.otherArtículo en Scopuses_PE
ulima.areas.lineasdeinvestigacionDesarrollo empresarial / Marketing y comportamiento del consumidores_PE
dc.identifier.journaleLifees_PE
dc.publisher.countryGBes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.identifier.doihttps://doi.org/10.7554/eLife.72930
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
ulima.cat009
ulima.autor.afiliacionUniversidad de Limaes_PE
ulima.autor.carreraIngeniería de Sistemases_PE


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