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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas484-494
Número de páginas11
ISBN (versión impresa)9783030762278
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online
Duración: 1 oct. 20203 oct. 2020

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1410 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia7th Annual International Conference on Information Management and Big Data, SIMBig 2020
CiudadVirtual, Online
Período1/10/203/10/20

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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