Exploring COVID-19 Trends in Mexico during the Winter Season with Explainable Artificial Intelligence (XAI)

Authors

Keywords:

Explainable Artificial Intelligence, XAI, Interpretable Random Forest, COVID-19, winter season, Mexico

Abstract

COVID-19 has become the most significant pandemic in recent years. Today, Mexico has recorded millions of infections and deaths since the pandemic started. Around the world, machine learning methods have been used to understand, predict or develop strategies to manage the virus and the pandemic. Although algorithms provide good results, it is necessary to understand why a model makes specific predictions with a particular data set. To explain this question, we apply Explainable Artificial Intelligence (XAI) in this paper. With this, it is possible to understand the characteristics that influence the model decisions when denoting between deaths and survivors. As a case of study, the positive cases detected during the winter season of 2020-2021 and 2021-2022 were considered. In this season, respiratory diseases increased considerably, and in the study period, they influenced the increase in positive cases and the spread of COVID-19. Preliminary results suggest that age is essential when using a Random Forest model. Preliminary results suggest that age is essential when determining the prognosis of a patient infected by COVID-19 in winter seasons.

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Author Biographies

Angélica Guzmán-Ponce, Universitat Jaume I

She was conferred a Ph.D. degree in Computer Science, from the Autonomous University of the State of Mexico in 2021. She is a member of the National System of Researchers (CONACYT) Level I. She is currently a postdoctoral researcher in the University Jaume I (Castellon de la Plana, Spain) and the Universitat Politècnica de València. Her research interests lie in Machine Learning and Graph Theory.

Rosa María Valdovinos-Rosas, Universidad Autónoma del Estado de México

She was conferred a Ph.D. degree in Computer Science. She is a member of the National System of Researchers (CONACYT) Nivel II and the AMEXCOMP. She has been a Full-Time Lecturer-Researcher at the Autonomous University of the State of Mexico (UAEMex). She contributes to the strengthening and consolidation of the scientific community through the training of quality human resources and disseminating knowledge and science in academic-scientific events at a national and international level. Furthermore, she has published scientific articles in specialized journals, book chapters with prestigious publishers, and dissemination articles.

Jacobo Leonardo González-Ruíz, Universidad Autónoma del Estado de México

Full-Time Professor at the Autonomous University of the State of Mexico. Member of the CONACYT, Level C. In 2008 he obtained the title of Computer Engineer and completed the master’s degree in engineering sciences in 2014. In 2018, he obtained the degree of PhD in Engineering Sciences from the Autonomous University of the State of Mexico.

Iván Franciso-Valencia, Universidad Autónoma del Estado de México

He obtained a Ph.D. degree in Engineering Sciences from the Autonomous University of the State of Mexico in 2021 and is a Level C member of the National System of Researchers (CONACYT). Currently, he is a postdoctoral researcher at the Toluca Institute of Technology, Mexico. His research interests include Combinatorial Game Theory, Optimization, and Artificial Intelligence.

J. Raymundo Marcial-Romero, Universidad Autónoma del Estado de México

He received his PhD in Computational Science from Birmingham University in 2005. He has been a Full-Time Lecturer-Researcher at the Department of Engineering of the Autonomous University of the State of Mexico. He is a member of the National System of Researchers (CONACYT) and Nivel I. His research interest includes approximation theory, computational complexity and graph theory.

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Published

2024-06-16

How to Cite

Guzmán-Ponce, A., Valdovinos-Rosas, R. M., González-Ruíz, J. L., Franciso-Valencia, I., & Marcial-Romero, J. R. (2024). Exploring COVID-19 Trends in Mexico during the Winter Season with Explainable Artificial Intelligence (XAI). IEEE Latin America Transactions, 22(7), 539–547. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8595

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