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Main arrow Archive of previous Issues arrow 4 2016 (50) arrow USING SELF-ORGANIZING KOHONEN MAPS TO ANALYZE THE RUSSIAN REGIONS IN TERMS OF SOCIALLY SIGNIFICANT DISEASES
USING SELF-ORGANIZING KOHONEN MAPS TO ANALYZE THE RUSSIAN REGIONS IN TERMS OF SOCIALLY SIGNIFICANT DISEASES Print
Tuesday, 26 July 2016

DOI: 10.21045/2071-5021-2016-50-4-9

Narkevich A.N.1, Serov A.A.2, Vinogradov K.A.1, Narkevich A.A.1, Shadrin K.V.1
1Krasnoyarsk State Medical University named after Professor V. F. Voyno-Yasenetsky, Ministry of Health of the Russian Federation, Krasnoyarsk
2Tver State University, Ministry of Education and Science of the Russian Federation, Tver

Contacts: Artem N. Narkevich, e-mail: This e-mail address is being protected from spam bots, you need JavaScript enabled to view it

Abstract. Significance. In the context of severe shortage of budgets at all levels, planning expenditures on certain areas of the national economy including healthcare, is extremely relevant.

The purpose of the study was to explore capabilities of self-organizing Kohonen maps for clustering regions according to their morbidity rates with socially significant diseases to identify regions that require strengthening of measures to control socially significant diseases.

Methods. To cluster regions we used national statistics on morbidity with socially significant diseases in 2006 and 2012 in 79 regions of the Russian Federation. Regions were clustered using Kohonen self-organizing maps, implemented in BaseGroup Labs Deductor Studio computer platform. According to the results the regions were divided into 3 clusters: favorable, in transition and problem clusters.

Results. According to results of the cluster analysis of regions in 2006, situation with socially significant diseases in Russia is rather favorable; the majority of regions were assigned to the transition cluster. Based on results of the 2012 clustering the following conclusion was made: problem clusters and clusters in transition tend to shrink, while the number of the regions in the favorable cluster grows. Target boundary indicators were identified to change cluster for a more favorable one as exemplified by the Krasnoyarsk and Tver regions.

Conclusions. The considered approach towards clustering regions according to data on morbidity with socially significant diseases using Kohonen self-organizing maps helps to determine current state of the region regarding these indicators, tendency towards changing position of the region in the Russian Federation and to identify target boundary indicators to improve situation with socially significant diseases.

Scope of application. The proposed approach to cluster regions and define boundary indicators can be used to identify regions that require development of measures to control specific socially significant diseases.

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Keywords: regions clustering; artificial neural network; socially significant diseases; Kohonen self-organizing maps.

References

  1. Artyukhov I.P., Shul'min A.V., Denisov V.S., Kozlov V.V. Planirovanie resursov zdravookhraneniya munitsipal'nogo obrazovaniya s funktsioniruyushchim vakhtovym poselkom [Planning of health care resources of the municipality with a functioning shift camps]. Sibirskoe meditsinskoe obozrenie 2012;(5):78-81. (In Russian)
  2. Botvin G., Zaboev M. Klasterizatsiya stran po makroekonomicheskim pokazatelyam s ispol'zovaniem apparata iskusstvennykh neyronnykh setey [Clustering country performance using artificial neural networks] // RISK: resursy, informatsiya, snabzhenie, konkurentsiya 2011;(4):552-556. (In Russian)
  3. Kulichenko V.P., Polubentseva E.I., Rakhaeva I.V., Chertukhina O.B. Planirovanie okazaniya meditsinskoy pomoshchi, kak instrument upravleniya sistemoy zdravookhraneniya regiona [Planning for health care, as a management tool of the health system in the region] // Vestnik Sankt-Peterburgskogo universiteta. Seriya 11: Meditsina 2011;(1):190-200. (In Russian)
  4. Moskvicheva M.G., Shchepilina E.S., Shchetinin V.B., Yakushev A.M., Savishcheva I.P. Analiz sostoyaniya zdorov'ya naseleniya kak osnova planirovaniya meditsinskoy pomoshchi na regional'nom urovne [Analysis of the health status of the population as a basis for health care planning at the regional level] // Rossiyskaya akademiya meditsinskikh nauk. Byulleten' natsional'nogo nauchno-issledovatel'skogo instituta obshchestvennogo zdorov'ya 2014;(2):86-91. (In Russian)
  5. Ob utverzhdenii perechnya sotsial'no znachimykh zabolevaniy i perechnya zabolevaniy, predstavlyayushchikh opasnost' dlya okruzhayushchikh [Approval of the list of socially significant diseases and the list of diseases that pose a danger to others] postanovlenie Pravitel'stva Rossiyskoy Federatsii ot 01.12.2004 g. 715. [Online]. 2004 [cited 2015 Feb 14]. Available from: http://base.garant.ru/12137881/ (In Russian).
  6. Seregin V.I. Opredelenie prioritetov v voprosakh meditsinskoy profilaktiki neinfektsionnykh zabolevaniy na regional'nom urovne (po materialam oprosa v sisteme pervichnoy mediko-sanitarnoy pomoshchi) [Prioritizing issues of medical prevention of noncommunicable diseases at the regional level (based on the survey in primary care)]. Sotsial'nye aspekty zdorov'ya naseleniya [serial online] 2014 [cited 2015 Feb 14]; 37(3). Available from: http://vestnik.mednet.ru/content/view/571/30/lang,ru/ (In Russian).
  7. Khaykin S. Neyronnye seti. Polnyy kurs [Neural Networks. Full course]. M.: Vil'yams, 2006. 1104s. (In Russian)
  8. Park S.H., Hosoishi S., Ogata K., Kuboki Y. Clustering of ant communities and indicator species analysis using self-organizing maps. Comptes rendus biologies 2014;337(9):545-52.
  9. Taşdemir K., Milenov P., Tapsall B. Topology-based hierarchical clustering of self-organizing maps. IEEE transactions on neural networks 2011;22(3):474-85.

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