Fraudulent medical behavior detection based on hybrid approach

PAN Song-song; ZHANG Wei-jia

doi:10.3969/j.issn.1000-5641.2017.05.012

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Sep. 2017

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PAN Song-song, ZHANG Wei-jia. Fraudulent medical behavior detection based on hybrid approach[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 125-137. doi: 10.3969/j.issn.1000-5641.2017.05.012

Citation:

PAN Song-song, ZHANG Wei-jia. Fraudulent medical behavior detection based on hybrid approach[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 125-137. doi: 10.3969/j.issn.1000-5641.2017.05.012

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Fraudulent medical behavior detection based on hybrid approach

doi: 10.3969/j.issn.1000-5641.2017.05.012

School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2017-06-20
Publish Date: 2017-09-25

Abstract

Abstract

With continuous improvement of medical insurance system, coverage of medical insurance continues to expand. The normal operation of medical insurance funds has been closely related with the vital interests of the people. However, frequent occurrence of fraudulent behaviors such as frequent hospitalization, hospitalization decomposition, abnormal fees threaten the normal operation of funds. This paper firstly used random forest method to select different features according to different diseases. Then the paper applied CBLOF-based and improved CBLOF methods to detect abnormal fees. What's more, we utilized rule-based method to identity frequent hospitalization and hospitalization decomposition. Extensive experiments on real medical claim datasets demonstrate the effectiveness and efficiency of our proposal. Finally, this paper proposed a medical insurance fund supervisory system, which can display results of pivot analysis with the help of Echarts.
- outlier detection,
- local outlier factor,
- CBLOF,
- hospitalization decomposition

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References(25)

References

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