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Fuzzy Logic-Based COVID-19 and Other Respiratory Conditions Pre-clinical Diagnosis System

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Telematics and Computing (WITCOM 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1280))

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Abstract

The COVID-19 disease, caused by a new coronavirus known as SARS-CoV-2, has recently emerged and caused the death of thousands of persons all around the world. One of the main issues with the disease has been, on one hand, the saturation of the medical personnel, and on the other, the untimely search of medical attention by patients who could confuse the symptoms with other common respiratory conditions with similar symptomatology. Since AI approaches based on machine learning depend on large training datasets, currently neither easily accessible nor reliable, a COVID-19 pre-clinical diagnosis system using a fuzzy inference system is constructed, which is also capable of contrasting it with other respiratory conditions, particularly allergies, common cold and influenza. With the use of this fuzzy inference system, complex decisions in the medical field could be able to be determined more effectively.

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Acknowledgements

This work was partially supported by projects 20200378, 20201040, 20200259 and EDI grant, by Secretaría de Investigación y Posgrado, Instituto Politécnico Nacional, as well as by project 8285.20-P from Tecnológico Nacional de México/IT de Mérida.

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Authors and Affiliations

  1. Tecnológico Nacional de México/IT de Mérida, Departamento de Sistemas y Computación, Mérida, Mexico

    M. G. Orozco-del-Castillo, R. A. Novelo-Cruz, P. A. Mena-Zapata, E. Brito-Borges & A. E. Álvarez-Pacheco

  2. AAAI Student Chapter at Yucatán, México (AAAIMX), Association for the Advancement of Artificial Intelligence, Mérida, Mexico

    M. G. Orozco-del-Castillo, R. A. Novelo-Cruz, J. J. Hernández-Gómez, P. A. Mena-Zapata, E. Brito-Borges & A. E. Álvarez-Pacheco

  3. Instituto Politécnico Nacional, Centro de Desarrollo Aeroespacial, Mexico City, Mexico

    J. J. Hernández-Gómez & G. A. Yáñez-Casas

  4. Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería y Tecnologías Avanzadas, Mexico City, Mexico

    A. E. García-Gutiérrez

  5. Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco, Sección de Estudios de Posgrado e Investigación, Mexico City, Mexico

    G. A. Yáñez-Casas

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  1. M. G. Orozco-del-Castillo
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Correspondence to J. J. Hernández-Gómez .

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  1. Instituto Politécnico Nacional, México, Mexico

    Miguel Félix Mata-Rivera

  2. Instituto Politécnico Nacional, México, Mexico

    Roberto Zagal-Flores

  3. Universidad Mayor, Santiago de Chile, Chile

    Cristian Barria-Huidobro

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Orozco-del-Castillo, M.G. et al. (2020). Fuzzy Logic-Based COVID-19 and Other Respiratory Conditions Pre-clinical Diagnosis System. In: Mata-Rivera, M.F., Zagal-Flores, R., Barria-Huidobro, C. (eds) Telematics and Computing. WITCOM 2020. Communications in Computer and Information Science, vol 1280. Springer, Cham. https://doi.org/10.1007/978-3-030-62554-2_29

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