Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA

Abstract Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to in...

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Autores principales:	Sarah Quiñones, Aditya Goyal, Zia U. Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7296c6c712a34bb798f57123edb103be
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spelling	oai:doaj.org-article:7296c6c712a34bb798f57123edb103be2021-12-02T16:36:12ZGeographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA10.1038/s41598-021-85381-52045-2322https://doaj.org/article/7296c6c712a34bb798f57123edb103be2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85381-5https://doaj.org/toc/2045-2322Abstract Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013–2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.Sarah QuiñonesAditya GoyalZia U. AhmedNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sarah Quiñones Aditya Goyal Zia U. Ahmed Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
description	Abstract Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013–2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.
format	article
author	Sarah Quiñones Aditya Goyal Zia U. Ahmed
author_facet	Sarah Quiñones Aditya Goyal Zia U. Ahmed
author_sort	Sarah Quiñones
title	Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
title_short	Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
title_full	Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
title_fullStr	Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
title_full_unstemmed	Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
title_sort	geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (t2d) prevalence in the usa
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7296c6c712a34bb798f57123edb103be
work_keys_str_mv	AT sarahquinones geographicallyweightedmachinelearningmodelforuntanglingspatialheterogeneityoftype2diabetesmellitust2dprevalenceintheusa AT adityagoyal geographicallyweightedmachinelearningmodelforuntanglingspatialheterogeneityoftype2diabetesmellitust2dprevalenceintheusa AT ziauahmed geographicallyweightedmachinelearningmodelforuntanglingspatialheterogeneityoftype2diabetesmellitust2dprevalenceintheusa
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Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA

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