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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

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dc.contributor.author Carrillo Larco, Rodrigo Martín
dc.contributor.author Castillo-Cara, Manuel
dc.contributor.author Anza-Ramirez, Cecilia
dc.contributor.author Bernabé Ortiz, Antonio
dc.date.accessioned 2021-04-13T20:50:58Z
dc.date.available 2021-04-13T20:50:58Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/20.500.12866/9117
dc.description.abstract INTRODUCTION: 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. en_US
dc.language.iso eng
dc.publisher BMJ Publishing Group
dc.relation.ispartofseries BMJ Open Diabetes Research and Care
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject adult en_US
dc.subject diabetes mellitus en_US
dc.subject developing countries en_US
dc.subject type 2 en_US
dc.title Clusters of people with type 2 diabetes in the general population: unsupervised machine learning approach using national surveys in Latin America and the Caribbean en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1136/bmjdrc-2020-001889
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.18
dc.relation.issn 2052-4897


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