Clustering Proposal Support for the COVID-19 Making Decision Process in a Data Demanding Scenario



Artificial Intelligence, Clustering, Artificial Neural Networks, Decision Support Systems, COVID-19


The COVID-19 disease surprised the world in the last months due to the number of infections and deaths have been increased in an exponential way. Since the pandemic was established by the World Health Organization, different strategies have been proposed for dealing diverse problems in cities that the coronavirus affected. This work presents a method to decision making support processes, specifically in environment with few data and variables to be considered. Thus, artificial neural networks architectures were employed to cluster the information available in the Bogota city, and provide a tool that allows generating additional findings in a simultaneous mode, and expressed as a visual map. The present proposal reached sensitivity measures around 75%, obtaining 100% for the best cases.


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Author Biographies

Alvaro David Orjuela-Cañon, School of Medicine and Health Sciences, Universidad del Rosario

Alvaro D. Orjuela-Cañón received the B.Sc degree in electronic engineering in Bogotá, Colombia in 2006 from Universidad Distrital Francisco José de Caldas. M.Sc degree in electrical engineering from Universidade Federal de Rio de Janeiro (COPPE/UFRJ) in Brazil in 2009. At the same, he was with Electrical Energy Research Centre (CEPEL) in Brazil. In 2015 earned his Ph. D degree in COPPE/UFRJ with study subject related to support the diagnosis of pleural and meningeal Tuberculosis, employing computational intelligence. He is principal professor in the School of Medicine and Health Sciences from Universidad del Rosario in Bogotá, Colombia. He is recognized as associate researcher by Colciencias (Colombian department of science, technology and innovation). In his topics of interest are signal processing, neural networks, machine learning, and artificial intelligence in health as support diagnosis methods. Currently, he is Senior Member of IEEE and volunteer of the Colombian chapter of the IEEE Computational Intelligence Society (CIS). He had published around 50 papers in specialized journals and conferences. In addition, he had oriented more than 20 students of undergraduate and graduate studies.

Oscar Perdomo, School of Medicine and Health Sciences, Universidad del Rosario

Oscar J. Perdomo received the B.Sc degree in electronic engineering in Neiva, Colombia in 2009 from Universidad Surcolombiana. M.Sc degree in biomedical engineering from Universidade Federal de Santa Catarina in Florianopolis, Brazil in 2012. In 2020 earned his Ph. D degree in systems and computation from Universidad Nacional de Colombia, Colombia. Currently, he is assistant professor in the School of Medicina and Health Sciences in Universidad del Rosario in Bogota, Colombia. In his topics of interest are: biomedical engineering, bioinstrumentation, Big Data, machine learning and deep learning in health.


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How to Cite

Orjuela-Cañon, A. D., & Perdomo, O. (2021). Clustering Proposal Support for the COVID-19 Making Decision Process in a Data Demanding Scenario. IEEE Latin America Transactions, 19(6), 1041–1049. Retrieved from



Special Issue on Fighting against COVID-19