The determinants of health assessment in the United States

Guillaume Coqueret ()
Additional contact information
Guillaume Coqueret: EM - EMLyon Business School

Post-Print from HAL

Abstract: In this article, we exploit a large dataset of surveys to answer a simple questions: which factors drive good (or bad) health? Using a set of 14 very diverse predictors (both socioeconomic and physiological), we perform sets of supervised learning tasks to determine which variables best explain the self-assessment of health conditions. Our predictive algorithms range from simple regressions to tabular networks and include random forests, all of which allow for some interpretability, directly or indirectly, either via feature importance or via conditional permutation influence of the trained models. Our results indicate that two indicators, in particular, emerge as potent determinants of physical well-being: income and exercise. The body mass index is the third main driver, though its role is less prominent. Importantly, for reproducibility, the dataset used in the study is in open access.

Keywords: Feature importance; Tabular networks; Descriptive analytics; Permutation importance; Supervised learning; Data mining (search for similar items in EconPapers)
Date: 2022-11-01
Note: View the original document on HAL open archive server: https://hal.science/hal-05618498v1
References: Add references at CitEc
Citations:

Published in Healthcare Analytics, 2022, 9 p. ⟨10.1016/j.health.2022.100106⟩

Downloads: (external link)
https://hal.science/hal-05618498v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05618498

DOI: 10.1016/j.health.2022.100106

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().