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

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-meansquared-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.

The application of artificial intelligence in health financing: a scoping review

“…It will not only allow employers and insurance companies to design suitable insurance schemes for the provision of healthcare benefits but will also prevent financial losses in the long run [ 75 ]. Using clustering techniques in this field will also provide opportunities and solutions for decision-makers to monitor insurance coverage based on socioeconomic, geospatial, and demographic variables in general and health insurance in particular [ 76 – 78 ].…”