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Title: ON MAP AND STATISTICAL DATA DRIVEN DECISION MAKING IN HEALTHCARE ORGANIZATION
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ON MAP AND STATISTICAL DATA DRIVEN DECISION MAKING IN HEALTHCARE ORGANIZATION
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1314-2704
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English
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18
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2.2
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In the work we consider the problem of optimal organization of healthcare system taking into account both map and statistical data. Map data include degrees of urbanization which classify the character of an area. This approach is based on mapping the territory by a grid square cell of 1 km2. Data about public health include research concerning spreading of basic infectious diseases and distribution of hospital system. Decision making is based on application of decision tree induction algorithm for tuples containing both numerical and categorized data. Map data were aggregated before they were used in learning tuples. Statistical data concerning public health were categorized with help of quantile classification method. We use a collection of functions of R software environment to visualize spatial urbanization data and models on top of static maps from various online sources. It includes tools common to those tasks, including functions for aggregation of spatial data and implementation of algorithm of decision tree induction in C5.0 package.
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conference
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18th International Multidisciplinary Scientific GeoConference SGEM 2018
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18th International Multidisciplinary Scientific GeoConference SGEM 2018, 02-08 July, 2018
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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331-338
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02-08 July, 2018
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website
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cdrom
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643
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decision tree; degree of urbanization; public health; R; ggmap
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