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

•A personalized algorithmic decision support tool is proposed for health insurance.•Facilitating premium pricing for women by considering gestational diabetes mellitus.•Reducing the premium for most patients with a lower risk of GDM.•The lower premium can motivate more patients to obtain insurance.•A smaller fraction of patients pay more while benefiting from an earlier diagnosis. 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.