Prediction for Collusive Fraud in Health Insurance using Machine Learning
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Machine LearningAbstract
Their most recent update was prompted by a false need that a few blackmailers intend to use for future upgrades. Current information-based and quantifiable plans have limited ability to see drive from clinical thought due to the imagined system for managing acting's proximity to standard clinical visits and the absence of truly checked data. The huge interest cycle should be especially yanked to ensure its exactness.
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References
J. Li, K.-Y. Huang, J. Jin and J. Shi, "A survey on statistical methods for health carefraud detection", Health Care Manage. Sci.,
vol. 11, no. 3, pp. 275-287, 2008.
L. Akoglu, M. McGlohon and C. Faloutsos, "OddBall: Spotting anomalies in weighted graphs", Proc. Pacific-Asia Conf. Knowl. Discov. Data Mining, pp.
-421, 2010.
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