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EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 1 (January 2024), Pages: 207-216


 Original Research Paper

Detection and risk assessment of COVID-19 through machine learning


 B. Luna-Benoso, J. C. Martínez-Perales *, J. Cortés-Galicia, U. S. Morales-Rodríguez


 Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico City, Mexico

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 * Corresponding Author. 

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COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal of this study is to use machine learning to identify if a person has COVID-19 based on their symptoms and to predict how severe their illness might become. This could lead to outcomes like needing a ventilator or being admitted to an Intensive Care Unit. The methods used in this research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification and Regression Trees, and Random Forests. Data from the National Epidemiological Surveillance System of Mexico City was analyzed. The findings indicate that the Multi-Layer Perceptron model was the most accurate, with an 87.68% success rate. It was best at correctly identifying COVID-19 cases. Random Forests were more effective at predicting severe cases and those requiring Intensive Care Unit admission, while Classification and Regression Trees were more accurate in identifying patients who needed to be put on a ventilator.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (


 Machine learning, Artificial neural networks, Decision trees, Random forests, COVID-19

 Article history

 Received 28 July 2023, Received in revised form 5 January 2024, Accepted 15 January 2024


The authors would like to thank the Instituto Politécnico Nacional (Secretaría Académica, COFAA, EDD, EDI, SIP, and ESCOM) and CONAHCYT for their financial support in developing this work.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


 Luna-Benoso B, Martínez-Perales JC, Cortés-Galicia J, and Morales-Rodríguez US (2024). Detection and risk assessment of COVID-19 through machine learning. International Journal of Advanced and Applied Sciences, 11(1): 207-216

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 Table 1 


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