Data mining techniques were used to investigate the use of knowledge extraction in predicting customer churn in insurance companies. Data were included from a health insurance company for providing insight into churn behaviour based on a design and application of a prediction model. Additionally, three promising data mining techniques were identified for the prediction of modeling, including logistic regression, neural network, and K-means. The decision tree method was used in the modeling phase of CRISP-DM for identifying the attributes of churned customers. The predictive analysis task is undertaken through classification and regression techniques. K-means clustering variation is selected for exploring if the clustering algorithms categorize the customers in churning and non-churning groups with homogeneous profiles. The findings of the study show that data mining procedures can be very successful in extracting hidden information and get to know customer's information. The 50:50 training set distribution resulted in effective outcomes when the logistic regression technique was used throughout this study. A 70:30 distribution worked effectively for the neural network technique. In this regard, it is concluded that each technique works effectively with a different training set distribution. The predicted findings can have direct implications for the marketing department of the selected insurance company, whereas the models are anticipated to be readily applicable in other environments via this data mining approach. This study has shown that the prediction models can be utilized throughout a health insurance company's marketing strategy and in a general academic context with a combination of a research-based emphasis with a business problem-solving approach.

数据挖掘技术被用来研究知识提取在预测保险公司客户流失方面的应用。数据来自一家健康保险公司，用于根据预测模型的设计和应用深入了解流失行为。此外，还确定了三种有前景的数据挖掘技术用于建模预测，包括逻辑回归、神经网络和 K 均值。在 CRISP-DM 的建模阶段使用决策树方法来识别流失客户的属性。预测分析任务是通过分类和回归技术进行的。选择 K 均值聚类变体来探索聚类算法是否将客户分类为具有同质配置文件的流失和非流失组。研究结果表明，数据挖掘程序在提取隐藏信息和了解客户信息方面非常成功。当整个研究中使用逻辑回归技术时，50:50 的训练集分布产生了有效的结果。70:30 的分布对神经网络技术有效。在这方面，可以得出结论，每种技术在不同的训练集分布下都能有效地工作。预测结果可以对选定保险公司的营销部门产生直接影响，而通过这种数据挖掘方法，预计这些模型可以很容易地应用于其他环境。这项研究表明，预测模型可以在整个健康保险公司的