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

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Abstract

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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Author notes

  1. Marlon David Arango-Abella and Juan Carlos Figueroa-García contributed equally to this work.

Authors and Affiliations

  1. Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Marlon David Arango-Abella & Juan Carlos Figueroa-García

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  1. Marlon David Arango-Abella
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  2. Juan Carlos Figueroa-García
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Correspondence to Marlon David Arango-Abella.

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This article is part of the topical collection “Recent Advances of AI, Optimization and Simulation” guest edited by Juan Carlos Figueroa-García, Roman Neruda, José Luis Villa Ramirez, Carlos Franco and Germán Hernández-Pérez.

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Arango-Abella, M.D., Figueroa-García, J.C. Classification of Users of a Health Service Provider Using Unsupervised Machine Learning Methods. SN COMPUT. SCI. 5, 543 (2024). https://doi.org/10.1007/s42979-024-02685-9

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