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

27 Pages Posted: 14 May 2020 Last revised: 4 Sep 2020

See all articles by Christos Makridis

Christos Makridis

Stanford University; Arizona State University (ASU); Massachusetts Institute of Technology (MIT) - Sloan School of Management; Department of Veterans Affairs (VA)

David Zhao

Stanford University, School of Engineering, Computer Science, Students

Cosmin (Adi) Bejan

Vanderbilt University

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, particularly with the spread of coronavirus. We leverage 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 over 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 (80.2% 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 Bejan, Cosmin (Adi) 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)

Stanford University ( email )

Stanford, CA 94305
United States

Arizona State University (ASU) ( email )

Farmer Building 440G PO Box 872011
Tempe, AZ 85287
United States

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

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

Department of Veterans Affairs (VA) ( email )

810 Vermont Avenue NW
Washington, DC 20420
United States

David Zhao

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

Stanford, CA
United States

Cosmin (Adi) Bejan

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Gil Alterovitz

Department of Veterans Affairs (VA) ( email )

810 Vermont Avenue NW
Washington, DC 20420
United States

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