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.
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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
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DOI: https://doi.org/10.1007/978-3-030-11890-7_53
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