Signs and symptoms analysis of SARS-CoV-2 virus infection waves

Authors

  • Felipe Cassemiro Ulrichsen State University of Rio de Janeiro
  • Alexandre Costa Sena State University of Rio de Janeiro
  • Luís Cristóvão Porto State University of Rio de Janeiro
  • Karla Figueiredo State University of Rio de Janeiro

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1339

Keywords:

COVID-19, SARS-CoV-2, Machine Learning

Abstract

Objective: Develop a Data Mining and Machine Learning methodology for COVID-19 diagnosis. Method: Create diagnostic models, evaluate differences in symptoms between pandemic waves. Results: Diagnose symptomatic SARS-CoV-2 infection. Conclusion: Highlight the effectiveness of the methodology in pandemic management.

Author Biographies

Felipe Cassemiro Ulrichsen, State University of Rio de Janeiro

PhD student, Matematical and Statistics Institute, State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.

Alexandre Costa Sena, State University of Rio de Janeiro

PhD/Associate Professor, Matematical and Statistics Institute, State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.

Luís Cristóvão Porto, State University of Rio de Janeiro

PhD/Associate Professor, Piquet Carneiro University Polyclinic (PPC), State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

Karla Figueiredo, State University of Rio de Janeiro

PhD/Associate Professor, Matematical and Statistics Institute, State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.

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Published

2024-11-19

How to Cite

Ulrichsen, F. C., Sena, A. C., Porto, L. C., & Figueiredo, K. (2024). Signs and symptoms analysis of SARS-CoV-2 virus infection waves. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1339

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