Leveraging Machine Learning to Characterize the Role of Socio-economic Determinants of Physical Health and Well-being Among Veterans

24 Pages Posted: 14 May 2020 Last revised: 26 May 2020

See all articles by Christos Makridis

Christos Makridis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

David Zhao

Stanford University, School of Engineering, Computer Science, Students

Gil Alterovitz

Department of Veterans Affairs (VA)

Date Written: April 19, 2020

Abstract

Understanding the contribution of demographic, socio-economic, and geographic characteristics as determinants of physical health and well-being is important for guiding public health policies and preventative behavior interventions. We use several machine learning methods to build predictive models of overall well-being and physical health among veterans as a function of these three sets of characteristics. We link Gallup's U.S. Daily Poll between 2014 and 2017 covering a range of demographic and socio-economic characteristics with zipcode characteristics from the Census Bureau to build predictive models of overall and physical well-being. Although the predictive models of overall well-being have weak performance, our classification of low levels of physical well-being performed better. Gradient boosting delivered the best results (90.2% precision, 82.4% recall, and 80.4% AUROC) with perceptions of purpose in the workplace and financial anxiety as the most predictive features. Our results suggest that additional measures of socio-economic characteristics are required to better predict physical well-being, particularly among vulnerable groups, like veterans. Reliable and effective predictive models will provide opportunities to create real-time and personalized feedback to help individuals improve their quality of life.

Keywords: Artificial intelligence, machine learning, medical informatics, subjective well-being, veterans

Suggested Citation

Makridis, Christos and Zhao, David and Alterovitz, Gil, Leveraging Machine Learning to Characterize the Role of Socio-economic Determinants of Physical Health and Well-being Among Veterans (April 19, 2020). Available at SSRN: https://ssrn.com/abstract=3580340 or http://dx.doi.org/10.2139/ssrn.3580340

Christos Makridis (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

David Zhao

Stanford University, School of Engineering, Computer Science, Students ( email )

Stanford, CA
United States

Gil Alterovitz

Department of Veterans Affairs (VA) ( email )

810 Vermont Avenue NW
Washington, DC 20420
United States

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