Diagnosis of SARS-CoV-2 Based on Patient Symptoms and Fuzzy Classifiers

Fray L. Becerra-Suarez, Heber I. Mejia-Cabrera, Víctor A. Tuesta-Monteza, Manuel G. Forero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The contention, mitigation and prevention measures that governments have implemented around the world do not appear to be sufficient to prevent the spread of SARS-CoV-2. The number of infected and dead continues to rise every day, putting a strain on the capacity and infrastructure of hospitals and medical centers. Therefore, it is necessary to develop new diagnostic methods based on patients' symptoms that allow the generation of early warnings for appropriate treatment. This paper presents a new method in development for the diagnosis of SARS-CoV-2, based on patient symptoms and the use of fuzzy classifiers. Eleven (11) variables were fuzzified. Then, knowledge rules were established and finally, the center of mass method was used to generate the diagnostic results. The method was tested with a database of clinical records of symptomatic and asymptomatic SARS-CoV-2 patients. By testing the proposed model with data from symptomatic patients, we obtained 100% sensitivity and 100% specificity. Patients according to their symptoms are classified into two classes, allowing for the detection of patients requiring immediate attention from those with milder symptoms.

Original languageEnglish
Title of host publicationInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditorsJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030762278
StatePublished - 2021
Externally publishedYes
Event7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online
Duration: 1 Oct 20203 Oct 2020

Publication series

NameCommunications in Computer and Information Science
Volume1410 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference7th Annual International Conference on Information Management and Big Data, SIMBig 2020
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Coronavirus
  • Covid-19
  • Diagnosis
  • Fuzzy classifier
  • SARS-CoV-2


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