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.

中文翻译：

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健康险个性化算法定价决策支持工具：妊娠期糖尿病分层两组案例


我们提出了一种个性化算法决策支持 (PADS) 工具，通过考虑妊娠期糖尿病 (GDM) 的风险，促进孕妇的溢价定价。 PADS 的保险费是根据机器学习算法的真实负值和正值比率得出的。具有统一设计的混合采样提高了机器学习算法在不平衡数据下的性能。特征选择方法保证了预测模型的准确性和可解释性。 PADS 降低了大多数 GDM 风险较低的患者的保费。一小部分患者会根据 PADS 支付更多保费；然而，他们可以从早期 GDM 诊断中受益。