Next Article in Journal
Characteristics of Unemployed People, Training Attendance and Job Searching Success in the Valencian Region (Spain)
Next Article in Special Issue
An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components
Previous Article in Journal
Improving the Quality of Survey Data Documentation: A Total Survey Error Perspective
Previous Article in Special Issue
Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market
Article Menu

Export Article

Open AccessArticle

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.
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)
  |  
PDF [2861 KB, uploaded 2 November 2018]
  |  

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-Text
Keywords: healthcare; medical insurance; prediction task; neural-like structures; Ito decomposition; Successive Geometric Transformations Model; non-iterative training algorithm healthcare; medical insurance; prediction task; neural-like structures; Ito decomposition; Successive Geometric Transformations Model; non-iterative training algorithm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top