Classification of Users of a Health Service Provider Using Unsupervised Machine Learning Methods

被引:0
|
作者
Marlon David Arango-Abella
Juan Carlos Figueroa-García
机构
[1] Universidad Distrital Francisco José de Caldas,Faculty of Engineering
关键词
Machine learning; Unsupervised classification; Health services provider; Geospatial data;
D O I
10.1007/s42979-024-02685-9
中图分类号
学科分类号
摘要
In this paper, we compare three unsupervised classification methods: k-means, fuzzy clustering and Self-Organized Maps (SOM) on a database of a health service provider in Bogotá–Colombia in order to classify users who request services in different offices and to propose a reorganization of human resources of all offices according to the density of customers and their needs. To do so, the database is pre-processed to correct some data problems such as incomplete individuals, bad measurements and outliers to then apply the three selected clustering methods, compare their results and finally propose some recommendations for improving service levels and to reduce both total service and waiting times.
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