Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset
© 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. I...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
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Public Library of Science
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/33226 |
| _version_ | 1848753886168350720 |
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| author | Luo, W. Nguyen, T. Nichols, M. Tran, The Truyen Rana, S. Gupta, S. Phung, D. Venkatesh, S. Allender, S. |
| author_facet | Luo, W. Nguyen, T. Nichols, M. Tran, The Truyen Rana, S. Gupta, S. Phung, D. Venkatesh, S. Allender, S. |
| author_sort | Luo, W. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T08:31:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-33226 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:31:38Z |
| publishDate | 2015 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-332262017-09-13T15:30:00Z Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset Luo, W. Nguyen, T. Nichols, M. Tran, The Truyen Rana, S. Gupta, S. Phung, D. Venkatesh, S. Allender, S. © 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/33226 10.1371/journal.pone.0125602 Public Library of Science fulltext |
| spellingShingle | Luo, W. Nguyen, T. Nichols, M. Tran, The Truyen Rana, S. Gupta, S. Phung, D. Venkatesh, S. Allender, S. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| title | Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| title_full | Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| title_fullStr | Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| title_full_unstemmed | Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| title_short | Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| title_sort | is demography destiny? application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset |
| url | http://hdl.handle.net/20.500.11937/33226 |