Personalized algorithmic pricing decision support tool for health insurance: The case of stratifying gestational diabetes mellitus into two groups

Abstract

We propose a personalized algorithmic decision support (PADS) tool, facilitating premium pricing for pregnant women by accounting for the risk of gestational diabetes mellitus (GDM). The insurance premium with PADS is derived from true negative and positive ratios of machine learning algorithms. Hybrid sampling with uniform designs improves ML algorithm performance under unbalanced data. Feature selection approaches guarantee the accuracy and interpretability of the prediction models. PADS reduces the premium for most patients with a lower risk of GDM. A smaller fraction of patients will pay more premiums under PADS; however, they can benefit from an earlier GDM diagnosis.