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Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

Luo, Wei, Nguyen, Thin, Nichols, Melanie, Tran, Truyen, Rana, Santu, Gupta, Sunil, Phung, Dinh, Venkatesh, Svetha and Allender, Steve 2015, Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset, PLoS One, vol. 10, no. 5, Article Number : e0125602, pp. 1-13, doi: 10.1371/journal.pone.0125602.

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Title Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset
Author(s) Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Nichols, MelanieORCID iD for Nichols, Melanie orcid.org/0000-0002-7834-5899
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Allender, SteveORCID iD for Allender, Steve orcid.org/0000-0002-4842-3294
Journal name PLoS One
Volume number 10
Issue number 5
Season Article Number : e0125602
Start page 1
End page 13
Total pages 13
Publisher Public Library of Science (PLOS)
Place of publication San Francisco, Calif.
Publication date 2015
ISSN 1932-6203
Summary For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.
Language eng
DOI 10.1371/journal.pone.0125602
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Public Library of Science (PLOS)
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073474

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.