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A Comparative Study of Machine Learning Methods to Predict COVID-19

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Innovations in Machine and Deep Learning

Part of the book series: Studies in Big Data ((SBD,volume 134))

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Abstract

First appearing in Wuhan City, Hubei region, China, the COVID-19 disease has threatened public health, trade, and the global economy. The World Health Organization has recommended testing for COVID-19 using a Reverse Transcription Polymerase Chain Reaction (RT-PCR) protocol to address diverse viral genes. Nevertheless, these test protocols demand RNA extraction kits, expensive machines, and trained technicians to operate them. Therefore, alternatives that are faster to diagnose, cheaper, and easier to access for patients and medical personnel are needed. This chapter presents a comparative analysis of machine-learning techniques for detecting COVID-19. The following four classifiers were trained, tested, and compared using the cross-validation technique with five folds: Random Forest, Stochastic Gradient Descent, Naive Bayes, and K- Nearest Neighbors. The dataset used in this project was the one the Government of Mexico has made available on the Internet on the Datos Abiertos Dirección General de Epidemiología web page. The results indicate that the Random Forest classifier performs best based on the area under the curve and the precision-recall curve metrics.

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Authors and Affiliations

  1. Universidad Autónoma de Ciudad Juárez, Av. José de Jesús Macías Delgado 18100, 32579, Ciudad Juárez, Chihuahua, Mexico

    J. Patricia Sánchez-Solís, Juan D. Mata Gallegos, Karla M. Olmos Sánchez & Victoria González Demoss

Authors
  1. J. Patricia Sánchez-Solís
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  2. Juan D. Mata Gallegos
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  3. Karla M. Olmos Sánchez
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  4. Victoria González Demoss
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Correspondence to J. Patricia Sánchez-Solís .

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Editors and Affiliations

  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Universidad Tecnológica de La Habana, La Habana, Cuba

    Alejandro Rosete

  3. School of Engineering, University of Cadiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Instituto Tecnológico de Ciudad Madero, Tecnológico Nacional de México, Tamaulipas, Mexico

    Nelson Rangel-Valdez

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Sánchez-Solís, J.P., Mata Gallegos, J.D., Olmos Sánchez, K.M., González Demoss, V. (2023). A Comparative Study of Machine Learning Methods to Predict COVID-19. In: Rivera, G., Rosete, A., Dorronsoro, B., Rangel-Valdez, N. (eds) Innovations in Machine and Deep Learning. Studies in Big Data, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-031-40688-1_15

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