Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs†
1
Department of Publishing Information Technologies, Lviv Polytechnic National University, 79000 Lviv, Ukraine
2
Department of Automated Control Systems, Lviv Polytechnic National University, 79000 Lviv, Ukraine
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of the paper: Vitynskyi, P.; Tkachenko, R.; Izonin, I.; Kutucu, H. Hybridization of the SGTM Neural-like Structure through Inputs Polynomial Extension. In Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 21–25 August 2018; Lviv Polytechnic Publishing House: Lviv, Ukraine, 2018; pp. 386–391.
Data 2018, 3(4), 46; https://doi.org/10.3390/data3040046
Received: 23 September 2018 / Revised: 24 October 2018 / Accepted: 29 October 2018 / Published: 31 October 2018
(This article belongs to the Special Issue Data Stream Mining and Processing)
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. View Full-TextKeywords:
healthcare; medical insurance; prediction task; neural-like structures; Ito decomposition; Successive Geometric Transformations Model; non-iterative training algorithm
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Tkachenko, R.; Izonin, I.; Vitynskyi, P.; Lotoshynska, N.; Pavlyuk, O. Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs. Data 2018, 3, 46.
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