Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans. (June 2021)

• Record Type: Journal Article
• Authors: Makridis, Christos A.; Zhao, David Y.; Bejan, Cosmin A.; Alterovitz, Gil
• Is Part Of: Computers in biology and medicine. Volume 133(2021)
• Publication Date: 2021-06
• Subjects: Health informatics -- Machine learning -- Subjective well-being -- Socioeconomics -- Veterans; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104354
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Introduction: We investigate the contribution of demographic, socio-economic, and geographic characteristics as determinants of physical health and well-being to guide public health policies and preventative behavior interventions (e.g., countering coronavirus). Methods: We use machine learning 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. Results: 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. Conclusion: Socio-economic characteristics explain large differences in physical and overall well-being. Effective predictive models that incorporate socio-economic data will provide opportunities to create real-time and personalized feedback to help individuals improve their quality of life. Highlights: We examine how demographic, socio-economic, and geographic characteristics affect physical and overall well-being among veterans. We leverage machine learning to build predictive models of physical and overall well-being among veterans as a function of these variables. Gradient boosting delivered the best results (80.2% precision, 82.4% recall, and 80.4% AUROC). Socio-economic characteristics, namely perceptions of purpose in the workplace and financial anxiety, emerged as the most predictive. Our results suggest that new socio-economic variables will help predict physical well-being, particularly for vulnerable groups.