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Extending cluster-based ensemble learning through synthetic population generation for modeling disparities in health insurance coverage across Missouri

Author

Listed:
  • Erik D. Mueller

    (Saint Louis University)

  • J. S. Onésimo Sandoval

    (Saint Louis University)

  • Srikanth P. Mudigonda

    (Saint Louis University)

  • Michael Elliott

    (Saint Louis University)

Abstract

In a previous study, Mueller et al. (ISPRS Int J Geo-Inf 8(1):13, 2019), presented a machine learning ensemble algorithm using K-means clustering as a preprocessing technique to increase predictive modeling performance. As a follow-on research effort, this study seeks to test the previously introduced algorithm’s stability and sensitivity, as well as present an innovative method for the extraction of localized and state-level variable importance information from the original dataset, using a nontraditional method known as synthetic population generation. Through iterative synthetic population generation with similar underlying statistical properties to the original dataset and exploration of the distribution of health insurance coverage across the state of Missouri, we identified variables that contributed to decisions for clustering, variables that contributed most significantly to modeling health insurance distribution status throughout the state, and variables that were most influential in optimizing model performance, having the greatest impact on change-in-mean-squared-error (MSE) measurements. Results suggest that cluster-based preprocessing approaches for machine learning algorithms can result in significantly increased performance, and also demonstrate how synthetic populations can be used for performance measurement to identify and test the extent to which variable statistical properties within a dataset can vary without resulting in significant performance loss.

Suggested Citation

  • Erik D. Mueller & J. S. Onésimo Sandoval & Srikanth P. Mudigonda & Michael Elliott, 2019. "Extending cluster-based ensemble learning through synthetic population generation for modeling disparities in health insurance coverage across Missouri," Journal of Computational Social Science, Springer, vol. 2(2), pages 271-291, July.
    • Handle: RePEc:spr:jcsosc:v:2:y:2019:i:2:d:10.1007_s42001-019-00047-7
    DOI: 10.1007/s42001-019-00047-7
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    References listed on IDEAS

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    1. Haas, J.S. & Lee, L.B. & Kaplan, C.P. & Sonneborn, D. & Phillips, K.A. & Liang, S.-Y., 2003. "The Association of Race, Socioeconomic Status, and Health Insurance Status with the Prevalence of Overweight among Children and Adolescents," American Journal of Public Health, American Public Health Association, vol. 93(12), pages 2105-2110.
    2. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
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