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Mathematics
Volume 14
Issue 3
10.3390/math14030573
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Application of Machine Learning to Cluster Analysis of Diabetes Mortality at the Municipality Level in Mexico According to Sociodemographic Factors

by
Nelva N. Almanza-Ortega
1,
Carlos Fernando Moreno-Calderon
2,
Sandra Silvia Roblero-Aguilar
3,
Rodolfo Pazos-Rangel
4,
Joaquín Pérez-Ortega
2,*,
Vanesa Landero-Nájera
5 and
Víctor Augusto Castellanos-Escamilla
3
1
Secretaría de Ciencias, Humanidades, Tecnología e Innovación, SECIHTI, Mexico City 03940, Mexico
2
Cenidet, Tecnológico Nacional de México, Cuernavaca 62490, Mexico
3
IT Tlalnepantla, Tecnológico Nacional de México, Tlalnepantla 54070, Mexico
4
IT Cd. Madero, Tecnológico Nacional de México, Madero 89440, Mexico
5
Computer Systems, Universidad Politécnica de Apodaca, Apodaca 66600, Mexico
*
Author to whom correspondence should be addressed.
Mathematics 2026, 14(3), 573; https://doi.org/10.3390/math14030573 (registering DOI)
Submission received: 19 December 2025 / Revised: 22 January 2026 / Accepted: 27 January 2026 / Published: 5 February 2026
(This article belongs to the Special Issue Application of Artificial Intelligence, Machine Learning and Data Science in Industrial and Medical Domains)
Versions Notes

Abstract

In recent years, the mortality due to diabetes has increased around the world. In particular, diabetes is the second leading cause of mortality in Mexico, with a heterogeneous distribution of mortality rates at the municipality level. The objective of this study is the analysis of clusters of municipalities with similar values for sociodemographic indices and diabetes mortality. In this sense, an application is presented that was developed using a data science methodology and a machine learning algorithm called fuzzy c-means. For this research, 4,604,360 death certificates from 2019 to 2023 were assessed, among other official data. As a result of the analysis, two key indicators related to diabetes mortality were found, i.e., one is the percentage of population in poverty and the other is population density. The main results of this research are as follows: a direct correlation was found between population density and mortality, and an inverse correlation was found between population in poverty and mortality. In the study interval, it was observed that the cluster with less mortality showed an increase in mortality rate year after year. Finally, we consider that the tendencies found can be useful to public health authorities for optimizing the distribution of resources for treating diabetes and reducing diabetes-related mortality.
Keywords: clustering; data science; diabetes; epidemiology; fuzzy c-means; machine learning clustering; data science; diabetes; epidemiology; fuzzy c-means; machine learning

Share and Cite

MDPI and ACS Style

Almanza-Ortega, N.N.; Moreno-Calderon, C.F.; Roblero-Aguilar, S.S.; Pazos-Rangel, R.; Pérez-Ortega, J.; Landero-Nájera, V.; Castellanos-Escamilla, V.A. Application of Machine Learning to Cluster Analysis of Diabetes Mortality at the Municipality Level in Mexico According to Sociodemographic Factors. Mathematics 2026, 14, 573. https://doi.org/10.3390/math14030573

AMA Style

Almanza-Ortega NN, Moreno-Calderon CF, Roblero-Aguilar SS, Pazos-Rangel R, Pérez-Ortega J, Landero-Nájera V, Castellanos-Escamilla VA. Application of Machine Learning to Cluster Analysis of Diabetes Mortality at the Municipality Level in Mexico According to Sociodemographic Factors. Mathematics. 2026; 14(3):573. https://doi.org/10.3390/math14030573

Chicago/Turabian Style

Almanza-Ortega, Nelva N., Carlos Fernando Moreno-Calderon, Sandra Silvia Roblero-Aguilar, Rodolfo Pazos-Rangel, Joaquín Pérez-Ortega, Vanesa Landero-Nájera, and Víctor Augusto Castellanos-Escamilla. 2026. "Application of Machine Learning to Cluster Analysis of Diabetes Mortality at the Municipality Level in Mexico According to Sociodemographic Factors" Mathematics 14, no. 3: 573. https://doi.org/10.3390/math14030573

APA Style

Almanza-Ortega, N. N., Moreno-Calderon, C. F., Roblero-Aguilar, S. S., Pazos-Rangel, R., Pérez-Ortega, J., Landero-Nájera, V., & Castellanos-Escamilla, V. A. (2026). Application of Machine Learning to Cluster Analysis of Diabetes Mortality at the Municipality Level in Mexico According to Sociodemographic Factors. Mathematics, 14(3), 573. https://doi.org/10.3390/math14030573

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