Research on medical insurance fraud identification method based on multi-source datasets
DOI: 10.23977/jeis.2024.090307 | Downloads: 14 | Views: 458
Author(s)
Jinhong Zhang 1
Affiliation(s)
1 School of Economics and Management, Jinzhong College of Information, Jinzhong, Shanxi, 030800, China
Corresponding Author
Jinhong ZhangABSTRACT
Due to the fact that datasets pertaining to health insurance are typically stored in different departments and involve multiple databases, conventional data analysis and traditional fraud research methods often fall short in accurately identifying fraudulent activities. To address this challenge, this paper, on one hand, starts by recognizing the unique characteristics of various collected datasets. It employs a multi-source data fusion approach, initially combining their features. Subsequently, an exploratory data analysis is conducted on the fused dataset. Compared to previous single datasets, the merged dataset contains more features, significantly enhancing the model's fitting performance. This approach maximizes the utilization of information within the data, allowing for better exploitation of the data's potential.On the other hand, this paper integrates the strategy of active learning with traditional logistic regression methods, constructing a novel model. The model is initially trained on labeled datasets, and after multiple experiments, it was observed that the fitting accuracy of the active learning model, constructed using the BT strategy (a type of active learning sample extraction strategy), surpassed that of a standalone logistic regression model. This innovative approach provides a new avenue for improving the accuracy of health insurance fraud detection.
KEYWORDS
Insurance fraud; Active learning; Logistic RegressionCITE THIS PAPER
Jinhong Zhang, Research on medical insurance fraud identification method based on multi-source datasets. Journal of Electronics and Information Science (2024) Vol. 9: 41-47. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090307.
REFERENCES
[1] Wang S L, Pai H T, Wu M F, et al. The evaluation of trustworthiness to identify health insurance fraud in dentistry[J]. Artificial intelligence in medicine, 2017, 75: 40-50.
[2] Thornton D. Brinkhuis M, Amrit C, et al. Categorizing and Describing the Types of Fraud in Healthcare [J]. Procedia Comput Science, 2015, 64(1): 713-720.
[3] Faseela V S, Thangam D P. A Review on Health Insurance Claim Fraud Detection[J]. International Journal of Engineering Research Science (IJOER), 2015, (4):47-49.
[4] Yip W, Hsiao W C. What Drove the Cycles of Chi- nese Health System Reforms[J]. Health System Re form, 2015, 1(1): 52-61.
[5] Yu, H. Universal Health Insurance Coverage for 1. 3 Billion[J]. Health Poli- People: What Accounts for China's Successcy, 2015, 119(9): 1145-1152.
[6] LIU J, BIER E, WILSON A, et al. Graph analysis for detecting fraud, waste, and abuse in healthcare data[C]// Proceedings of the 27th Conference on Innovative Applications of Artificial Intelli-gence. Palo Alto, CA: AAAI Press, 2015:3912-3919.
[7] Wilhelm W K. The Fraud Management Lifecycle Theory: A Holistic Approach to Fraud Management[J]. Journal of Eco- nomic Crime Management, 2004, 2(2): 1-38.
[8] Faseela V S, Thangam P. A Review on Health Insurance Claim Fraud Detection[J]. International Journal of Engi- neering Research Science, 2015, 1(1):47-49.
[9] Hubick, K. T.. Artificial neural networks in Australia. Canberra: Common wealth of Australia, 1992.
[10] Stijn Viaene, Richard A. Derrig, Guido Dedene. A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis. Ieee Transaction on Knowledge and Data Engineering, 2004: Vo1. 18, NO. 5
[11] Fletcher Lu, J. Efrim Boritz. Detecting fraud in health insurance data: learning to model incomplete benford's law distributions. Lecture notes in computer science, 2005(3720):633-640.
[12] Jing Li, Kuei-Ying Huang, Jionghua Jin, Jianjun Shi. A Survey on Statistical 51 Methods for Health Care Fraud Detection. Health Care Manage Science, 2007, (5):1-21.
[13] James H. Bisker, Kate Ehrlich. Health Insurance Fraud Detection Using Socia Network Analytics, United States Patent Application Publication. 2008:US 2008/0172257
[14] Ekina T, Leva F, Ruggeri F, et al. Application of bayesian methods in detection of healthcare frad[J]. chemical engineering Transaction, 2013, 33-42.
[15] Sadiq S, Tao Y, Yan Y, et al. Mining anomalies in medicare big data using patient rule inductionmethod[C]2017 IEEE third international conference on multimedia Big Data (BigMM). IEEE, 2017:185-192.
[16] Chuishi Meng, Xiuwen Yi, Lu Su, Jing Gao, and Yu Zheng. 2017. City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '17). Association for Computing Machinery, New York, NY, USA, Article 1, 1–10.
Downloads: | 10601 |
---|---|
Visits: | 361079 |
Sponsors, Associates, and Links
-
Information Systems and Signal Processing Journal
-
Intelligent Robots and Systems
-
Journal of Image, Video and Signals
-
Transactions on Real-Time and Embedded Systems
-
Journal of Electromagnetic Interference and Compatibility
-
Acoustics, Speech and Signal Processing
-
Journal of Power Electronics, Machines and Drives
-
Journal of Electro Optics and Lasers
-
Journal of Integrated Circuits Design and Test
-
Journal of Ultrasonics
-
Antennas and Propagation
-
Optical Communications
-
Solid-State Circuits and Systems-on-a-Chip
-
Field-Programmable Gate Arrays
-
Vehicular Electronics and Safety
-
Optical Fiber Sensor and Communication
-
Journal of Low Power Electronics and Design
-
Infrared and Millimeter Wave
-
Detection Technology and Automation Equipment
-
Journal of Radio and Wireless
-
Journal of Microwave and Terahertz Engineering
-
Journal of Communication, Control and Computing
-
International Journal of Surveying and Mapping
-
Information Retrieval, Systems and Services
-
Journal of Biometrics, Identity and Security
-
Journal of Avionics, Radar and Sonar