Indian Journal of Science and Technology

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Distance based Model to Detect Healthcare Insurance Fraud within Unsupervised Database


Affiliations
1 Engineer of Information Technology, NouAndish Pars Co., Tehran, Iran, Islamic Republic of
2 Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran, Islamic Republic of
3 Shahid Beheshti University of Medical Sciences, School of Medical Education, Tehran, Iran, Islamic Republic of
4 Engineer of Computer Engineering, Department of Computer Engineering, Malayer Branch, Islamic Azad University, Malayer, Iran, Islamic Republic of
 

Objectives: Healthcare fraud costs the country tens of billions of dollars a year. Methods: Fraudulent behaviors of healthcare providers and patients have become a serious burden to insurance systems by bringing unnecessary costs. Insurance companies thus developed methods to identify fraud. Results: In this paper a methodology offered based on data mining approach to discover fraud in healthcare insurance. Applications: To test and evaluate model real-world data set related to healthcare insurance in Iran has been used. Investment result of operation model on this data set indicates proper performance of it.

Keywords

Anomaly Detection, Data Mining, Healthcare Fraud, Outlier Detection, Unsupervised Method.
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  • Distance based Model to Detect Healthcare Insurance Fraud within Unsupervised Database

Abstract Views: 135  |  PDF Views: 0

Authors

Hojjat Ahmadinejad
Engineer of Information Technology, NouAndish Pars Co., Tehran, Iran, Islamic Republic of
Amir Norouzi
Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran, Islamic Republic of
Ahura Ahmadi
Shahid Beheshti University of Medical Sciences, School of Medical Education, Tehran, Iran, Islamic Republic of
Ali Yousefi
Engineer of Computer Engineering, Department of Computer Engineering, Malayer Branch, Islamic Azad University, Malayer, Iran, Islamic Republic of

Abstract


Objectives: Healthcare fraud costs the country tens of billions of dollars a year. Methods: Fraudulent behaviors of healthcare providers and patients have become a serious burden to insurance systems by bringing unnecessary costs. Insurance companies thus developed methods to identify fraud. Results: In this paper a methodology offered based on data mining approach to discover fraud in healthcare insurance. Applications: To test and evaluate model real-world data set related to healthcare insurance in Iran has been used. Investment result of operation model on this data set indicates proper performance of it.

Keywords


Anomaly Detection, Data Mining, Healthcare Fraud, Outlier Detection, Unsupervised Method.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i43%2F123707