Understanding cross-data dynamics of individual and social/environmental factors through a public health lens: explainable machine learning approaches.
GWLASSO
SHAP
influential factors
machine learning
obesity
Journal
Frontiers in public health
ISSN: 2296-2565
Titre abrégé: Front Public Health
Pays: Switzerland
ID NLM: 101616579
Informations de publication
Date de publication:
2023
2023
Historique:
received:
16
08
2023
accepted:
09
10
2023
medline:
14
11
2023
pubmed:
13
11
2023
entrez:
13
11
2023
Statut:
epublish
Résumé
The rising prevalence of obesity has become a public health concern, requiring efficient and comprehensive prevention strategies. This study innovatively investigated the combined influence of individual and social/environmental factors on obesity within the urban landscape of Seoul, by employing advanced machine learning approaches. We collected 'Community Health Surveys' and credit card usage data to represent individual factors. In parallel, we utilized 'Seoul Open Data' to encapsulate social/environmental factors contributing to obesity. A Random Forest model was used to predict obesity based on individual factors. The model was further subjected to Shapley Additive Explanations (SHAP) algorithms to determine each factor's relative importance in obesity prediction. For social/environmental factors, we used the Geographically Weighted Least Absolute Shrinkage and Selection Operator (GWLASSO) to calculate the regression coefficients. The Random Forest model predicted obesity with an accuracy of >90%. The SHAP revealed diverse influential individual obesity-related factors in each Gu district, although 'self-awareness of obesity', 'weight control experience', and 'high blood pressure experience' were among the top five influential factors across all Gu districts. The GWLASSO indicated variations in regression coefficients between social/environmental factors across different districts. Our findings provide valuable insights for designing targeted obesity prevention programs that integrate different individual and social/environmental factors within the context of urban design, even within the same city. This study enhances the efficient development and application of explainable machine learning in devising urban health strategies. We recommend that each autonomous district consider these differential influential factors in designing their budget plans to tackle obesity effectively.
Identifiants
pubmed: 37954048
doi: 10.3389/fpubh.2023.1257861
pmc: PMC10639162
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1257861Informations de copyright
Copyright © 2023 Jeong, Yun, Park and Mun.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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