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

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