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The method of fuzzy autoregression as an instrument of sociological forecasting of incidence rate of diabetes, blood circulatory system diseases and oncology in Russia during the XXI century
Varaksin Sergei Vladimirovich

PhD in Physics and Mathematics

Docent, the department of Algebra and Mathematical Logic, Altai State University     

656049, Russia, Altaiskii Krai oblast', g. Barnaul, ul. Lenina, 61

varaksin@bk.ru
Другие публикации этого автора
 

 
Varaksina Natal'ya Vladimirovna

PhD in Sociology

Docent, the department of State and Municipal Administration, Altai Branch of the Russian Presidential Academy of National Economy and Public Administration under the President of the Russian Federation

656066, Russia, Altaiskii krai, g. Barnaul, ul. Partizanskaya, 187

varaksins@yandex.ru
Goncharova Natal'ya Petrovna

PhD in Sociology

Docent, the department of State and Municipal Administration, Altai Branch of the Russian Presidential Academy of National Economy and Public Administration under the President of the Russian Federation

656008, Russia, Altaiskii krai, g. Barnaul, ul. Partizanskaya, 187

g-natalia@mail.ru

Abstract.

The subject of this research is modelling of a number of demographic parameters using the method of fuzzy autoregression as a tool of sociological studies. The object of this research is the dynamics of incidence rate of diabetes, blood circulatory system diseases and malignant tumors over the recent 15-25 years. This work determines the parameters of mathematical models or time sequences of the number of newly registered cases of diverse nosology. The conclusion is made on the need for development of targeted healthcare programs at the federal and regional levels. The main instrument of analysis is the structuring of forecasting mathematical models of changes in the incidence rate of diseases using the method of fuzzy autoregression and algorithm of MatLab system language. The methods of fuzzy nonlinear regression and autoregression have been applied in technical and natural sciences for over 40 years. In the area of social sciences, similar methods have not been used until recently, therefore their application in forecasting of demographic parameters, including the distribution of diseases is innovative.

Keywords: health care forecasting, incidence rates, sociological modeling, mathematical model, linear programming, fuzzy autoregression, fuzzy time series, time series, fuzzy number, incidence factors

DOI:

10.7256/2454-0684.2019.1.28514

Article was received:

29-12-2018


Review date:

26-12-2018


Publish date:

01-01-2019


This article written in Russian. You can find full text of article in Russian here .

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