TY - JOUR
T1 - Cluster analysis of social determinants of health and HIV/AIDS knowledge among Peruvian youths using Kohonen’s self-organized maps
T2 - A data-exploration study based on a Demographic and health survey
AU - Aybar Flores, Alejandro
AU - Talavera, Álvaro
AU - Espinoza-Portilla, Elizabeth
N1 - Publisher Copyright:
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2025/1/17
Y1 - 2025/1/17
N2 - Background: Human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) have evolved into a global development burden, with nearly 40 million infections and 25 million deaths. Compared to other age groups, youth have increased risks of contracting the disease due to social and health structural factors; thus, additional efforts are needed to effectively tackle the challenges associated with this age group. Epidemiological studies employing unsupervised learning techniques are essential for shaping public health policies. Objective: This study aimed to describe the Peruvian youth population based on their sociodemographic, health, and economic characteristics using an unsupervised learning approach through the development of a neural network model based on Kohonen’s self-organizing maps (SOMs), allowing the identification of social profiles in the study population. Methods: This quantitative study used data from the 2019 Peruvian Demographic and Family Health Survey. An SOM network model for clustering individuals with similar attributes and clustering prototype vectors based on the agglomerative hierarchical clustering (AHC) method and their visualization on an SOM was applied to the study sample. Results: Clustering of prototype vectors yielded four clusters, each of which represented a profile of Peruvian youths based on their knowledge of HIV/AIDS and structural health determinants. Conclusions: Kohonen’s neural networks allowed the identification of patterns and behaviors among youths in Peru, quantifying and characterizing the four social clusters regarding HIV/AIDS and their social determinants. Kohonen’s maps may benefit healthcare professionals and policymakers by offering a useful method for tailoring interventions and policies based on the detected profiles, thereby enhancing the visibility of these focal points at the national level.
AB - Background: Human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) have evolved into a global development burden, with nearly 40 million infections and 25 million deaths. Compared to other age groups, youth have increased risks of contracting the disease due to social and health structural factors; thus, additional efforts are needed to effectively tackle the challenges associated with this age group. Epidemiological studies employing unsupervised learning techniques are essential for shaping public health policies. Objective: This study aimed to describe the Peruvian youth population based on their sociodemographic, health, and economic characteristics using an unsupervised learning approach through the development of a neural network model based on Kohonen’s self-organizing maps (SOMs), allowing the identification of social profiles in the study population. Methods: This quantitative study used data from the 2019 Peruvian Demographic and Family Health Survey. An SOM network model for clustering individuals with similar attributes and clustering prototype vectors based on the agglomerative hierarchical clustering (AHC) method and their visualization on an SOM was applied to the study sample. Results: Clustering of prototype vectors yielded four clusters, each of which represented a profile of Peruvian youths based on their knowledge of HIV/AIDS and structural health determinants. Conclusions: Kohonen’s neural networks allowed the identification of patterns and behaviors among youths in Peru, quantifying and characterizing the four social clusters regarding HIV/AIDS and their social determinants. Kohonen’s maps may benefit healthcare professionals and policymakers by offering a useful method for tailoring interventions and policies based on the detected profiles, thereby enhancing the visibility of these focal points at the national level.
KW - VIH/SIDA
KW - Determinantes sociales de la salud
KW - Aprendizaje no supervisado
KW - Kohonen
KW - Mapas autoorganizados
KW - Agrupamiento jerárquico aglomerativo
KW - Juventud
KW - Perú
KW - HIV/AIDS
KW - Social determinants of health
KW - Unsupervised learning
KW - Kohonen
KW - Self-organizing maps
KW - Agglomerative hierarchical clustering
KW - Youth
KW - Peru
KW - social determinants of health
KW - self-organizing maps
KW - agglomerative hierarchical clustering
KW - youth
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85215509139&partnerID=8YFLogxK
U2 - 10.1080/16549716.2024.2438070
DO - 10.1080/16549716.2024.2438070
M3 - Article in a journal
SN - 1654-9716
VL - 17
SP - 20
JO - Global Health Action
JF - Global Health Action
IS - 1
M1 - 2438070
ER -