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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Roman Tkachenko & Ivan Izonin & Pavlo Vitynskyi & Nataliia Lotoshynska & Olena Pavlyuk, 2018. "Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs," Data, MDPI, vol. 3(4), pages 1-14, October.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:46-:d:179471
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    References listed on IDEAS

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    1. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
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    2. Jiapeng Yan & Huifang Kong & Zhihong Man, 2022. "Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles," Energies, MDPI, vol. 15(24), pages 1-17, December.

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