Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset
journal contributionposted on 2015-01-01, 00:00 authored by Wei LuoWei Luo, Thin NguyenThin Nguyen, Melanie NicholsMelanie Nichols, Truyen TranTruyen Tran, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, Steven AllenderSteven Allender
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.