Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)
Joint Authors
Asiedu, Louis
Sowah, Robert A.
Koumadi, Koudjo M.
Kuuboore, Marcellinus
Ofoli, Abdul
Kwofie, Samuel
Apeadu, Kwaku O.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-09-02
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services.
In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services.
Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies.
The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated.
This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies.
Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used.
The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers.
Three GSVM classifiers were evaluated and their results compared.
Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).
American Psychological Association (APA)
Sowah, Robert A.& Kuuboore, Marcellinus& Ofoli, Abdul& Kwofie, Samuel& Asiedu, Louis& Koumadi, Koudjo M.…[et al.]. 2019. Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs). Journal of Engineering،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1173513
Modern Language Association (MLA)
Sowah, Robert A.…[et al.]. Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs). Journal of Engineering No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1173513
American Medical Association (AMA)
Sowah, Robert A.& Kuuboore, Marcellinus& Ofoli, Abdul& Kwofie, Samuel& Asiedu, Louis& Koumadi, Koudjo M.…[et al.]. Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs). Journal of Engineering. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1173513
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173513