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The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data

  • Richard A. Bauder
  • Taghi M. Khoshgoftaar
Research

Abstract

Healthcare in the United States is a critical aspect of most people’s lives, particularly for the aging demographic. This rising elderly population continues to demand more cost-effective healthcare programs. Medicare is a vital program serving the needs of the elderly in the United States. The growing number of Medicare beneficiaries, along with the enormous volume of money in the healthcare industry, increases the appeal for, and risk of, fraud. In this paper, we focus on the detection of Medicare Part B provider fraud which involves fraudulent activities, such as patient abuse or neglect and billing for services not rendered, perpetrated by providers and other entities who have been excluded from participating in Federal healthcare programs. We discuss Part B data processing and describe a unique process for mapping fraud labels with known fraudulent providers. The labeled big dataset is highly imbalanced with a very limited number of fraud instances. In order to combat this class imbalance, we generate seven class distributions and assess the behavior and fraud detection performance of six different machine learning methods. Our results show that RF100 using a 90:10 class distribution is the best learner with a 0.87302 AUC. Moreover, learner behavior with the 50:50 balanced class distribution is similar to more imbalanced distributions which keep more of the original data. Based on the performance and significance testing results, we posit that retaining more of the majority class information leads to better Medicare Part B fraud detection performance over the balanced datasets across the majority of learners.

Keywords

Medicare fraud Class imbalance Random undersampling Big data 

Notes

Authors' contributions

The authors would like to thank the Editor-in-Chief and the two reviewers for their insightful evaluation and constructive feedback of this paper, as well as the members of the Data Mining and Machine Learning Laboratory, Florida Atlantic University, for their assistance in the review process. We acknowledge partial support by the NSF (CNS-1427536). Opinions, findings, conclusions, or recommendations in this paper are the authors’ and do not reflect the views of the NSF. All authors read and approved the final manuscript.

Competing interests

All authors declare that they have no Competing interests.

Ethics approval and consent to participate

The article does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Engineering & Computer ScienceFlorida Atlantic UniversityBoca RatonUSA

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