Risk assessment for health insurance using equation modeling and machine learning

Abstract

Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

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

“…This enables the support of various applications in different domains, including energy and resource management, intelligent transportation systems, and e-health [ 84 ]. New vectors can also be added to the health insurance packages to help the health and insurance sector construct mathematical risk equation models with parameters to map real-life risk conditions [ 85 ]. Resource allocation strategies can be developed for prioritizing limited healthcare capacity based on the computational characterization of spatiotemporal patterns of the disease transmission risks [ 86 ].…”