Abstract
This paper presents an improved outlier detection algorithm based on K-means clustering to identify suspicious medical fraud in medical insurance audits. This paper elaborates how to preprocess medical insurance data for medical insurance fraud, and puts forward the improved principle and process of outlier detection algorithm based on K-means clustering. The experiment is carried out by using real medical insurance data, and the efficiency of the algorithm conducted a test.
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