Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs). (2nd September 2019)

• Record Type: Journal Article
• Authors: Sowah, Robert A.; Kuuboore, Marcellinus; Ofoli, Abdul; Kwofie, Samuel; Asiedu, Louis; Koumadi, Koudjo M.; Apeadu, Kwaku O.
• Other Names: Iqbal Kamran Academic Editor.
• Is Part Of: Journal of engineering. Volume 2019(2019)
• Publication Date: 2019-09-02
• Subjects: Engineering -- Periodicals; 620.005
• DOI: 10.1155/2019/1432597
• Languages: English
• ISSNs: 2314-4904

Abstract

Abstract : 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%).