Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)
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Date
2019-09-02
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Engineering
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. (e 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. (is 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.(eexperimental results have proven that the GSVM possessed better detection and classification performance
when applied using SVM kernel classifiers. (ree 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%).
Description
Research Article
Keywords
Fraud in health insurance, decision support system (DSS), Genetic support vector machines (GSVMs), National Health Insurance Scheme