Statistical and machine learning methods have become paramount in order to handle large size claims data as part of health care fraud detection frameworks. Among these, predictive methods such as regression and classification algorithms are widely used with labeled data. However, the imbalanced nature of health care claims data and skewness of fraud distributions result with challenges in practical applications. This paper presents the use of various classification algorithms and data pre-processing methods on claim payment populations and overpayment scenarios with different characteristics. It can help the health care practitioners evaluate the advantages and disadvantages of these analytical methods, and choose the right classification method and apply them properly for their specific circumstances. We utilize publicly available U.S. Medicare Part B health care claims payment data from the hospitals with a number of fraud label scenarios to demonstrate potential fraud patterns. We discuss the computational demand and accuracy of the methods.

中文翻译：

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实践中的医疗保健欺诈分类器

作为医疗保健欺诈检测框架的一部分，为了处理大量索赔数据，统计和机器学习方法已变得至关重要。其中，回归和分类算法等预测方法被广泛用于标记数据。然而，医疗索赔数据的不平衡性和欺诈分布的偏度导致实际应用面临挑战。本文介绍了各种分类算法和数据预处理方法对不同特征的索赔支付人群和多付场景的使用。它可以帮助医疗保健从业者评估这些分析方法的优缺点，选择正确的分类方法并根据具体情况正确应用。我们利用公开可用的美国 来自医院的 Medicare B 部分医疗保健索赔支付数据，其中包含许多欺诈标签场景，以展示潜在的欺诈模式。我们讨论了这些方法的计算需求和准确性。