Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 17 02 2023
accepted: 20 07 2023
medline: 17 8 2023
pubmed: 15 8 2023
entrez: 15 8 2023
Statut: epublish

Résumé

To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

Identifiants

pubmed: 37582086
doi: 10.1371/journal.pone.0289553
pii: PONE-D-23-04755
pmc: PMC10427001
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0289553

Informations de copyright

Copyright: © 2023 Marqués-Sánchez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Pilar Marqués-Sánchez (P)

Faculty of Health Sciences, SALBIS Research Group, Campus de Ponferrada, Universidad de León, León, Spain.

María Cristina Martínez-Fernández (MC)

Faculty of Health Sciences, SALBIS Research Group, Campus de Ponferrada, Universidad de León, León, Spain.

José Alberto Benítez-Andrades (JA)

Department of Electric, SALBIS Research Group, Systems and Automatics Engineering, Universidad de León, León, León, Spain.

Enedina Quiroga-Sánchez (E)

Faculty of Health Sciences, SALBIS Research Group, Campus de Ponferrada, Universidad de León, León, Spain.

María Teresa García-Ordás (MT)

SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, León, León, Spain.

Natalia Arias-Ramos (N)

Faculty of Health Sciences, SALBIS Research Group, Campus de Ponferrada, Universidad de León, León, Spain.

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