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An Evaluation of Unsupervised Machin...

da Rosa, Raquel C.

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An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program./
作者:	da Rosa, Raquel C.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	64 p.
附註:	Source: Masters Abstracts International, Volume: 79-12.
Contained By:	Masters Abstracts International79-12.
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10815097
ISBN:	9780438013520

An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program.
da Rosa, Raquel C.

An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 64 p.

Source: Masters Abstracts International, Volume: 79-12.

Thesis (M.S.)--Florida Atlantic University, 2018.

This item must not be sold to any third party vendors.

The population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which indicates reasonable fraud detection results. We employ two unsupervised machine learning algorithms, Isolation Forest, and Unsupervised Random Forest, which have not been previously used for the detection of fraud and abuse on Medicare data. Additionally, we implement three other machine learning methods previously applied on Medicare data which include: Local Outlier Factor, Autoencoder, and k-Nearest Neighbor. For our dataset, we combine the 2012 to 2015 Medicare provider utilization and payment data and add fraud labels from the List of Excluded Individuals/Entities (LEIE) database. Results show that Local Outlier Factor is the best model to use for Medicare fraud detection.

ISBN: 9780438013520Subjects--Topical Terms:

1030799
Information Technology.

An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program.
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