使用数据挖掘方法检测医疗欺诈

Long-Sheng Chen, Jiachen Chen
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引用次数: 2

摘要

医疗欺诈行为使医疗保险支出逐年上升。这不仅增加了医疗和金融系统的负担,而且使许多有需要的人难以获得这些资源。因此,如何解决这一问题已成为关键问题之一。因此,本研究旨在通过数据挖掘方法建立医疗保险欺诈的预测模型,并试图发现影响欺诈的重要因素。在这项工作中,我们将使用决策树(DT),支持向量机(SVM)和反向传播神经网络(BPN)来建立分类模型。将对这三种方法进行比较。并且,我们将使用决策树来提取重要因素,这些因素可以为有效检测医疗欺诈提供重要信息。希望能有效减少医疗保险欺诈的负面影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Data Mining Methods to Detect Medical Fraud
Medical fraudulent activities have made medical insurance expenditures rise year by year. This not only increases the burden on the medical and financial system, but also makes it difficult for many people in need to obtain these resources. Therefore, how to solve this problem has become one of critical issues. Therefore, this study aims to establish a predictive model of medical insurance fraud through data mining methods, and attempts to discover important factors affecting fraud. In this work, we will use Decision Tree (DT), Support Vector Machines (SVM), and Back Propagation Neural Networks (BPN) to establish classification models. A comparison of these three methods will be done. And, we will use decision trees to extract important factors that could provide important information for effectively detect medical fraud. Hopefully, we can effectively reduce the negative impact of medical insurance fraud.
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