Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

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

    233284_233284.pdf (1.425Mb)
    Access Status
    Open access
    Authors
    Luo, W.
    Nguyen, T.
    Nichols, M.
    Tran, The Truyen
    Rana, S.
    Gupta, S.
    Phung, D.
    Venkatesh, S.
    Allender, S.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Luo, W. and Nguyen, T. and Nichols, M. and Tran, T.T. and Rana, S. and Gupta, S. and Phung, D. et al. 2015. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset. PLoS ONE. 10 (5).
    Source Title
    PLoS ONE
    DOI
    10.1371/journal.pone.0125602
    School
    Multi-Sensor Proc & Content Analysis Institute
    Remarks

    This open access article is distributed under the Creative Commons license http://creativecommons.org/licenses/by/4.0/

    URI
    http://hdl.handle.net/20.500.11937/33226
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Luo et al. 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.

    Related items

    Showing items related by title, author, creator and subject.

    • Burden of disease and benefits of exercise in fixed airway obstruction asthma
      Turner, Sian Elizabeth (2009)
      Background and research questions. The characterization of chronic persistent asthma in an older adult population is not well defined. This is due to the difficulties in separating the diagnosis of asthma from that of ...
    • Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
      Gupta, Sunil; Tran, The Truyen; Luo, W.; Phung, D.; Kennedy, R.; Broad, A.; Campbell, D.; Kipp, D.; Singh, M.; Khasraw, M.; Matheson, L.; Ashley, D.; Venkatesh, S. (2014)
      Objectives: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning ...
    • Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013
      Miller, Ted (2015)
      SummaryBackground Up-to-date evidence on levels and trends for age-sex-specific all-cause and cause-specific mortality is essential for the formation of global, regional, and national health policies. In the Global Burden ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.