New Support Vector Machines Research from University of Ghana Outlined

Engineering Business Daily

2019 SEP 26 (NewsRx) -- By a News Reporter-Staff News Editor at Engineering Business Daily -- New research on Support Vector Machines is the subject of a new report. According to news originating from Accra, Ghana, by NewsRx correspondents, research stated, “Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services.”

Funders for this research include Carnegie Corporation of New York.

The news editors obtained a quote from the research from University of Ghana: “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.”

According to the news editors, the research concluded: “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%).”

For more information on this research see: Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs). Engineering Business Daily, 2019,2019(). (Engineering Business Daily - https://www.hindawi.com/journals/je/). The publisher for Engineering Business Daily is Hindawi Limited.

A free version of this journal article is available at https://doi.org/10.1155/2019/1432597.

Our news journalists report that additional information may be obtained by contacting Robert A. Sowah, Department of Computer Engineering, University of Ghana, PMB 25, Legon, Accra, Ghana. Additional authors for this research include Marcellinus Kuuboore, Abdul Ofoli, Samuel Kwofie, Louis Asiedu, Koudjo M. Koumadi, Kwaku O. Apeadu.

(Our reports deliver fact-based news of research and discoveries from around the world.)