@inproceedings{c86e180d1e0b4841a59a7fd5c88ded58,
title = "A Comparison of Machine Learning Methods Applicable to Healthcare Claims Fraud Detection",
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.",
keywords = "Fraud detection, Healthcare, Machine learning",
author = "Nnaemeka Obodoekwe and {van der Haar}, {Dustin Terence}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; International Conference on Information Technology and Systems, ICITS 2019 ; Conference date: 06-02-2019 Through 08-02-2019",
year = "2019",
doi = "10.1007/978-3-030-11890-7_53",
language = "English",
isbn = "9783030118891",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "548--557",
editor = "Manolo Paredes and Carlos Ferr{\'a}s and {\'A}lvaro Rocha",
booktitle = "Information Technology and Systems - Proceedings of ICITS 2019",
address = "Germany",
}