Big Data-Driven Abnormal Behavior Detection in Healthcare Based on Association Rules

• Published in: IEEE access Vol. 8; pp. 129002 - 129011
• Main Authors: Zhou, Shengyao, He, Jie, Yang, Hui, Chen, Donghua, Zhang, Runtong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: abnormal behavior; Algorithms; association rules; Behavior; Big Data; Computer networks; Data mining; Data models; Distributed processing; Fraud; Health care; Health services; healthcare insurance; Insurance; Medical diagnostic imaging; Medical treatment; Pattern analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3009006

Healthcare insurance frauds are causing millions of dollars of public healthcare fund losses around the world in various ways, which makes it very important to strengthen the management of medical insurance in order to guarantee the steady operation of medical insurance funds. Healthcare fraud detection methods can reduce the losses of healthcare insurance funds and improve medical quality. Existing fraud detection studies mostly focus on finding normal behavior patterns and treat those violating normal behavior patterns as fraudsters. However, fraudsters can often disguise themselves with some normal behaviors, such as some consistent behaviors when they seek medical treatments. To address these issues, we combined a MapReduce distributed computing model and association rule mining to propose a medical cluster behavior detection algorithm based on frequent pattern mining. It can detect certain consistent behaviors of patients in medical treatment activities. By analyzing 1.5 million medical claim records, we have verified the effectiveness of the method. Experiments show that this method has better performance than several benchmark methods.