Statistical models of neural networks for medical insurance cost forecasting
Journal Title: Формування ринкових відносин в Україні. Збірник наукових праць - Year 2017, Vol 1, Issue 1
Abstract
Neural networks have become a popular method of non–linear statistical forecasting therefore it seems important to apply this method to forecasting the cost of health insurance. In this paper the network based on a stochastic model that has direct connection tiered architecture with casual connections between modules and frequency characteristics with obstacles. The resulting Bayesian method, derived by logical solution for this model is based on the Kalman filter. Thus resulting learning algorithm generalizes the so–called one–dimensional Newton method that implements the algorithm is now popular in the literature on neural networks. The article presents numerical prediction method for studying relative cost of health insurance in the form of chaotic time series of errors and shows higher forecasting accuracy of the new algorithm compared to the existing ones.
Authors and Affiliations
A. SIGAYOV
Keywords
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