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
Date Written: April 19, 2020
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
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