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Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset

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Listed:
  • Wei Luo
  • Thin Nguyen
  • Melanie Nichols
  • Truyen Tran
  • Santu Rana
  • Sunil Gupta
  • Dinh Phung
  • Svetha Venkatesh
  • Steve Allender

Abstract

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.

Suggested Citation

  • Wei Luo & Thin Nguyen & Melanie Nichols & Truyen Tran & Santu Rana & Sunil Gupta & Dinh Phung & Svetha Venkatesh & Steve Allender, 2015. "Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:plo:pone00:0125602
    DOI: 10.1371/journal.pone.0125602
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    References listed on IDEAS

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    1. van Hooijdonk, Carolien & Droomers, Mariël & van Loon, Jeanne A.M. & van der Lucht, Fons & Kunst, Anton E., 2007. "Exceptions to the rule: Healthy deprived areas and unhealthy wealthy areas," Social Science & Medicine, Elsevier, vol. 64(6), pages 1326-1342, March.
    2. Link, B.G. & Phelan, J.C., 1996. "Understanding sociodemographic differences in health--the role of fundamental social causes," American Journal of Public Health, American Public Health Association, vol. 86(4), pages 471-473.
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