A Mathematical Approach to Healthcare Insurance Data Analytics

The emergence of big data analytics as a way of deriving insights from data has brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the near absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. A prototype for the model was implemented using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, it was tested for accuracy using data from the National Health Insurance Scheme in Nigeria with a view to reducing delays in the processes of the Scheme. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the flow of resources.