Health care fraud classifiers in practice

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 such regression classification algorithms are widely used with labeled data. However, the imbalanced nature skewness distributions result challenges practical applications. This paper presents use various pre-processing on claim payment populations overpayment scenarios different characteristics. It can help practitioners evaluate advantages disadvantages these analytical methods, choose right method apply them properly for their specific circumstances. We utilize publicly available U.S. Medicare Part B from hospitals a number label demonstrate potential patterns. discuss computational demand accuracy methods.