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

dc.contributor.authorCarrillo Larco, Rodrigo M.
dc.contributor.authorAnza Ramírez, Cecilia
dc.contributor.authorBernabé Ortiz, Antonio
dc.contributor.authorCastillo Cara, José Manuel
dc.date.accessioned2024-05-20T11:35:08Z
dc.date.available2024-05-20T11:35:08Z
dc.date.issued2021-01-29
dc.description.abstractWe 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.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1136/bmjdrc-2020-001889
dc.identifier.issn2044-6055
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12262
dc.journal.issue1
dc.journal.titleBMJ Open
dc.journal.volume9
dc.language.isoen
dc.publisherBMJ Publishing Group
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.titleClusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbeanes
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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