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

Erik D. Mueller (), J. S. Onésimo Sandoval, Srikanth P. Mudigonda and Michael Elliott
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Erik D. Mueller: Saint Louis University
J. S. Onésimo Sandoval: Saint Louis University
Srikanth P. Mudigonda: Saint Louis University
Michael Elliott: Saint Louis University

Journal of Computational Social Science, 2019, vol. 2, issue 2, No 9, 291 pages

Abstract: 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.

Keywords: Synthetic population generation; Ensemble modeling; Machine learning; Model validation; Stability testing; Health disparities; Health insurance (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s42001-019-00047-7

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