Clusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbean

Carrillo Larco, Rodrigo M, Castillo Cara, Manuel, Anza Ramirez , Cecilia y Bernabé Ortiz, Antonio . (2021) Clusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbean. BMJ Open

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Título Clusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbean
Autor(es) Carrillo Larco, Rodrigo M
Castillo Cara, Manuel
Anza Ramirez , Cecilia
Bernabé Ortiz, Antonio
Materia(s) Informática
Abstract We aimed to identify clusters of people with type 2 diabetes mellitus (T2DM) and to assess whether the frequency of these clusters was consistent across selected countries in Latin America and the Caribbean (LAC). Research design and methods We analyzed 13 population-based national surveys in nine countries (n=8361). We used k-means to develop a clustering model; predictors were age, sex, body mass index (BMI), waist circumference (WC), systolic/diastolic blood pressure (SBP/DBP), and T2DM family history. The training data set included all surveys, and the clusters were then predicted in each country-year data set. We used Euclidean distance, elbow and silhouette plots to select the optimal number of clusters and described each cluster according to the underlying predictors (mean and proportions). Results The optimal number of clusters was 4. Cluster 0 grouped more men and those with the highest mean SBP/DBP. Cluster 1 had the highest mean BMI and WC, as well as the largest proportion of T2DM family history. We observed the smallest values of all predictors in cluster 2. Cluster 3 had the highest mean age. When we reflected the four clusters in each country-year data set, a different distribution was observed. For example, cluster 3 was the most frequent in the training data set, and so it was in 7 out of 13 other country-year data sets. Conclusions Using unsupervised machine learning algorithms, it was possible to cluster people with T2DM from the general population in LAC; clusters showed unique profiles that could be used to identify the underlying characteristics of the T2DM population in LAC.
Editor(es) BMJ Publishing Group
Fecha 2021-01-29
Formato application/pdf
Identificador bibliuned:557-Jmcastillo-0010
http://e-spacio.uned.es/fez/view/bibliuned:557-Jmcastillo-0010
DOI - identifier https://doi.org/10.1136/bmjdrc-2020-001889
ISSN - identifier 2044-6055
Nombre de la revista BMJ Open
Número de Volumen 9
Número de Issue 1
Publicado en la Revista BMJ Open
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/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 BMJ Open (2021) 29-1, está disponible en línea en el sitio web del editor: BMJ Publishing Group https://drc.bmj.com/content/9/1/e001889
Notas adicionales The registered version of this article, first published in BMJ Open (2021) 29-1, is available online at the publisher's website: BMJ Publishing Group https://drc.bmj.com/content/9/1 /e001889

 
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Creado: Wed, 28 Feb 2024, 21:07:14 CET