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Dissertation/Thesis Abstract

An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program
by da Rosa, Raquel C., M.S., Florida Atlantic University, 2018, 64; 10815097
Abstract (Summary)

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.

Indexing (document details)
Advisor: Khoshgoftaar, Taghi M.
Commitee: Nojoumian, Mehrdad, Wang, Dingding
School: Florida Atlantic University
Department: Computer Science
School Location: United States -- Florida
Source: MAI 57/06M(E), Masters Abstracts International
Subjects: Information Technology, Finance, Artificial intelligence
Keywords: Fraud, Insurance, Machine learning, Medicare, Outliers, Unsupervised
Publication Number: 10815097
ISBN: 978-0-438-01352-0
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