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ON MAP AND STATISTICAL DATA DRIVEN DECISION MAKING IN HEALTHCARE ORGANIZATION

Vasyl Martsenyuk

First published: 2018-06-20https://doi.org/10.5593/sgem2018/2.2/s08.042View metrics

Abstract

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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Publication details

Title
ON MAP AND STATISTICAL DATA DRIVEN DECISION MAKING IN HEALTHCARE ORGANIZATION
Authors
Vasyl Martsenyuk
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2018
Pages
331-338
SWS Citekey
Martsenyuk20188331338
ISSN
1314-2704
ISBN
978-619-7408-40-9
Language
en
Publication type
Conference Paper
Keywords
decision treedegree of urbanizationpublic healthRggmap
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