Clusters of people with type 2 diabetes in the general population: Unsupervised machine learning approach using national surveys in Latin America and the Caribbean
File(s)
Published version
Author(s)
Carrillo Larco, Rodrigo
Type
Journal Article
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
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
Date Acceptance
2020-12-20
Citation
BMJ Open Diabetes Research and Care, 9, pp.e001889-e001889
ISSN
2052-4897
Publisher
BMJ Publishing Group
Start Page
e001889
End Page
e001889
Journal / Book Title
BMJ Open Diabetes Research and Care
Volume
9
Copyright Statement
© Author(s) (or their employer(s)) 2021. Re-use
permitted under CC BY. Published by BMJ
permitted under CC BY. Published by BMJ
License URL
Sponsor
Wellcome Trust
Identifier
https://drc.bmj.com/content/9/1/e001889
Grant Number
214185/Z/18/Z
Subjects
adult
developing countries
diabetes mellitus
type 2
1103 Clinical Sciences
Publication Status
Published
Date Publish Online
2021-01-29