Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs

Roman Tkachenko, Ivan Izonin, Pavlo Vitynskyi, Nataliia Lotoshynska and Olena Pavlyuk
Additional contact information
Roman Tkachenko: Department of Publishing Information Technologies, Lviv Polytechnic National University, 79000 Lviv, Ukraine
Ivan Izonin: Department of Publishing Information Technologies, Lviv Polytechnic National University, 79000 Lviv, Ukraine
Pavlo Vitynskyi: Department of Publishing Information Technologies, Lviv Polytechnic National University, 79000 Lviv, Ukraine
Nataliia Lotoshynska: Department of Publishing Information Technologies, Lviv Polytechnic National University, 79000 Lviv, Ukraine
Olena Pavlyuk: Department of Automated Control Systems, Lviv Polytechnic National University, 79000 Lviv, Ukraine

Data, 2018, vol. 3, issue 4, 1-14

Abstract: The paper describes a new non-iterative linear supervised learning predictor. It is based on the use of Ito decomposition and the neural-like structure of the successive geometric transformations model (SGTM). Ito decomposition (Kolmogorov–Gabor polynomial) is used to extend the inputs of the SGTM neural-like structure. This provides high approximation properties for solving various tasks. The search for the coefficients of this polynomial is carried out using the fast, non-iterative training algorithm of the SGTM linear neural-like structure. The developed method provides high speed and increased generalization properties. The simulation of the developed method’s work for solving the medical insurance costs prediction task showed a significant increase in accuracy compared with existing methods (common SGTM neural-like structure, multilayer perceptron, Support Vector Machine, adaptive boosting, linear regression). Given the above, the developed method can be used to process large amounts of data from a variety of industries (medicine, materials science, economics, etc.) to improve the accuracy and speed of their processing.

Keywords: healthcare; medical insurance; prediction task; neural-like structures; Ito decomposition; Successive Geometric Transformations Model; non-iterative training algorithm (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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