MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry

Jenita Mary Arockiam, Angelin Claret Seraphim Pushpanathan


Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient’s challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent claims in the health insurance databases. The experimental results have proven that the MR-ISVM classifier outperforms better in classification and detection than other support vector machine (SVM) kernel classifiers. From the results, a positive impact seen in declining the computational time on processing the healthcare insurance claims without compromising the classification accuracy is achieved. The proposed MR-ISVM classifier achieves 87.73% accuracy than the linear (75.3%) and radial basis function (79.98%).


big data; fraud detection; insurance claims; iterative support vector machine; MapReduce framework;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).