Boldina K.S. Classification of Russian Regions by the Level of the Healthcare System Development
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Boldina Kristina Sergeevna
Master Student in Applied Informatics in Analytical Economy, Institute of Management and Regional Economy, Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Abstract. This article emphasizes the classification of regions by the level of the healthcare system development in the Russian Federation. The main goal of factorial and cluster analysis consists in classifying variables by homogenous groups (clusters). First of all, with the help of the regional statistics data on the indicators groups it is possible to carry out the factorial analysis on the basis of the main components. It is aimed at sorting out those factors, which have the biggest impact on the healthcare system development in these regions. The result
of this factorial analysis is the transition from a set of initial variables to a significantly smaller number of new variables – factors. A possible interpretation of the received results can be made after finding a matrix of factorial capacity, in which the first factor shows the development of infrastructure and healthcare. The second factor shows the general condition of the population's incidence. Subsequently, a cluster analysis was carried out with the help of the method that deals with the k-avereges on the two main components. These components were revealed during the factorial analysis to unite some objects in classes (clusters) in order to get the most similar objects in one cluster and ensure that those objects belonging to different classes can be distinguished from each other on the highest level possible. After carrying out the cluster analysis the following data was obtained: the first cluster includes 44 regions, the second cluster includes 38 regions. After that, a shedule of average values of the main factors on clusters was constructed and each cluster was characterized. The results showed that the first cluster emphasizes a high value of the first factor and a low value of the second one, while the second cluster shows a high value of the second one.
of this factorial analysis is the transition from a set of initial variables to a significantly smaller number of new variables – factors. A possible interpretation of the received results can be made after finding a matrix of factorial capacity, in which the first factor shows the development of infrastructure and healthcare. The second factor shows the general condition of the population's incidence. Subsequently, a cluster analysis was carried out with the help of the method that deals with the k-avereges on the two main components. These components were revealed during the factorial analysis to unite some objects in classes (clusters) in order to get the most similar objects in one cluster and ensure that those objects belonging to different classes can be distinguished from each other on the highest level possible. After carrying out the cluster analysis the following data was obtained: the first cluster includes 44 regions, the second cluster includes 38 regions. After that, a shedule of average values of the main factors on clusters was constructed and each cluster was characterized. The results showed that the first cluster emphasizes a high value of the first factor and a low value of the second one, while the second cluster shows a high value of the second one.
Key words: healthcare, main indicators, factorial analysis, cluster analysis, method of main components, matrix of factorial loadings.
Classification of Russian Regions by the Level of the Healthcare System Development by Boldina K.S. is licensed under a Creative Commons Attribution 4.0 International License.
Citation in English: Science Journal of Volgograd State University. Mathematics. Physics. №2 (27) 2015 pp. 31-39
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