Long-Sheng Chen
ABSTRACT
Medical fraudulent activities have made medical insurance expenditures rise year by year. This not only increases the burden on the medical and financial system, but also makes it difficult for many people in need to obtain these resources. Therefore, how to solve this problem has become one of critical issues. Therefore, this study aims to establish a predictive model of medical insurance fraud through data mining methods, and attempts to discover important factors affecting fraud. In this work, we will use Decision Tree (DT), Support Vector Machines (SVM), and Back Propagation Neural Networks (BPN) to establish classification models. A comparison of these three methods will be done. And, we will use decision trees to extract important factors that could provide important information for effectively detect medical fraud. Hopefully, we can effectively reduce the negative impact of medical insurance fraud.
- National Health Expenditure Projections 2017-2026. (2018) Centers for Medicare & Medicaid Services. [Online]. Available: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-andReports/NationalHealthExpendData/Downloads/ForecastSummary.pdfGoogle Scholar
- L. Morris, "Combating Fraud In Health Care: An Essential Component Of Any Cost Containment Strategy," 2009. [Online]. Available: https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.28.5.1351Google Scholar
- W. Zhang and X. He, "An Anomaly Detection Method for Medicare Fraud Detection," 2017 IEEE International Conference on Big Knowledge (ICBK), Hefei, 2017, pp. 309--314.Google Scholar
- R. Bauder and T. Khoshgoftaar, "Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data," 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 80--87.Google Scholar
- R. Bauder, R. da Rosa and T. Khoshgoftaar, "Identifying Medicare Provider Fraud with Unsupervised Machine Learning," 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 285--292.Google Scholar
- A. Tike and S. Tavarageri, "A medical price prediction system using hierarchical decision trees," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 3904--3913.Google Scholar
- Wijenayake, S., Graham, T., & Christen, P., 2018, "A Decision Tree Approach to Predicting Recidivism in Domestic Violence." Paper presented at the Trends and Applications in Knowledge Discovery and Data Mining, Cham.Google Scholar
- Roy, A. G., & Urolagin, S. (2019). "Credit Risk Assessment Using Decision Tree and Support Vector Machine Based Data Analytics." Paper presented at the Creative Business and Social Innovations for a Sustainable Future, Cham.Google Scholar
- Cortes, C. & Vapnik, V. Mach Learn (1995) 20: 273. https://doi.org/10.1007/BF00994018Google Scholar
- Bauder, Richard A. and Taghi M. Khoshgoftaar. "The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels." FLAIRS Conference (2018).Google Scholar
- Bahareh Kalantar, Biswajeet Pradhan, Seyed Amir Naghibi, Alireza Motevalli & Shattri Mansor, 2018, "Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)," Geomatics, Natural Hazards and Risk, 9:1, 49-69, DOI: 10.1080/19475705.2017.1407368Google ScholarCross Ref
- C. Chen, C. Kuo, S. Kuo and Y. Chou, "Dynamic Normalization BPN for Stock Price Forecasting," 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, 2015, pp. 2855--2860.Google Scholar
- Chen, R.-C., Lai, C.-Y., 2007, "Back propagation networks for predicting credit card fraud with stratified personalized data," WSEAS Transactions on Computers Volume 6, Issue 3, Pages 425-432Google Scholar
Index Terms
- Using Data Mining Methods to Detect Medical Fraud
Recommendations
Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry
Abstract IntroductionMachine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an ...
Data mining for credit card fraud: A comparative study
Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly ...
Data mining application for cyber credit-card fraud detection system
ICDM'13: Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspectsSince the evolution of the internet, many small and large companies have moved their businesses to the internet to provide services to customers worldwide. Cyber credit card fraud or no card present fraud is increasingly rampant in the recent years for ...
Comments