文章基本信息

标题：Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs
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作者：Roman Tkachenko , Ivan Izonin ; Pavlo Vitynskyi , Nataliia Lotoshynska ; Olena Pavlyuk 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2018
卷号：3
期号：4
页码：46-59
DOI：10.3390/data3040046
出版社：MDPI Publishing
摘要：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.
关键词：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