Abstract
The healthcare industry has become a very important pillar in the modern society but has witnessed an increase in fraudulent activities. Traditional fraud detection methods have been used to detect potential fraud, but for certain cases they have been insufficient and time consuming. Data mining which has emerged as a very important process in knowledge discovery has been successfully applied in the health insurance claims fraud detection. We implemented a prototype that comprised different methods and a comparison of each of the methods was carried out to determine which method is most suited for the Medicare dataset. We found that while ensemble methods and neural net performed, the logistic regression and the naive bayes model did not perform well as depicted in the result.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yu, H.: Impacts of rising health care costs on families with employment-based private insurance: a national analysis with state fixed effects. Health Serv. Res. 47(5), 2012–2030 (2012)
Singh, A.: Fraud in insurance on rise. Technical report, Ernst & Young (2011)
Davis, L.E.: Growing health care fraud drastically affects all of us, October 2017
Rabiul, J., Nabeel, M., Ahsan, H., Sifat, M.: An evaluation of data processing solutions considering preprocessing and “special” features. In: 11th International Conference on Signal-Image Technology & Internet-Based Systems (2015)
McLeod, S.: Maslow’s hierarchy of needs. Simply Psychol. 1 (2007)
Bush, J., Sandridge, L., Treadway, C., Vance, K., Coustasse, A.: Medicare fraud, waste and abuse. In: Business and Health Administration Association Annual Conference (2017)
Thornton, D., van Capelleveen, G., Poel, M., van Hillegersberg, J., Mueller, R.M.: Outlier-based health insurance fraud detection for U.S. medicaid data. In: 16th International Conference on Enterprise Information Systems (2014)
Branting, L.K., Reeder, F., Gold, J., Champney, T.: Graph analytics for healthcare fraud risk estimation. In: Advances in Social Networks Analysis and Mining (ASONAM) (2016)
Bauder, R.A., Khoshgoftaar, T.M.: A probabilistic programming approach for outlier detection in healthcare claims. 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (2016)
Bauder, R.A., Khoshgoftaar, T.M.: Medicare fraud detection using machine learning methods. In: 16th IEEE International Conference on Machine Learning and Applications (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Obodoekwe, N., van der Haar, D.T. (2019). A Comparison of Machine Learning Methods Applicable to Healthcare Claims Fraud Detection. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-11890-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11889-1
Online ISBN: 978-3-030-11890-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)