使用机器学习和可解释的人工智能定制医疗保险费

Manohar Kapse , Vinod Sharma , Rutuj Vidhale , Varun Vellanki
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引用次数: 0

摘要

本研究对不同客户群的健康保险保费进行分析。具体而言,它旨在确定影响健康保险费定价的因素,以及这些因素对不同客户群体的影响。该研究使用来自消费者调查的数据集,结合多种机器学习模型,分析和预测了不同年龄组、性别、健康状况、保单持续时间和保单成员人数所支付保费的重要性特征。最后,使用可解释的人工智能来预测每个变量在确定个人保险单价格时的权重。调查结果对有效影响医疗保险费定价的人口因素和生活方式等因素及其对不同客户群的影响提供了至关重要的见解。本研究的结果将有助于未来的购买者和决策者选择最佳的健康保险计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Customization of health insurance premiums using machine learning and explainable AI
This study presents an analysis of health insurance premiums across various customer segments. Specifically, it aims to identify the factors influencing the pricing of health insurance premiums, vis a vis their impact on different customer segments. Using a dataset from consumer surveys, coupled with multiple Machine Learning models, the study analyzed and predicted features of importance for premiums paid across various age groups, gender, health conditions, policy duration, and the number of members included in the policy. Finally, the explainable AI was used to predict the weightage of each variable in determining the price of the insurance policy for the individuals. The findings provide crucial insights into the factors such as demographic factors and lifestyle that effectively influence the pricing of health insurance premiums vis a vis their impact on various customer segments. The results of this study will assist prospective buyers and decision-makers in choosing the best health insurance plans.
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