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.
|Title of host publication||Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings|
|Editors||Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||11|
|State||Published - 2021|
|Event||7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online|
Duration: 1 Oct 2020 → 3 Oct 2020
|Name||Communications in Computer and Information Science|
|Conference||7th Annual International Conference on Information Management and Big Data, SIMBig 2020|
|Period||1/10/20 → 3/10/20|
Bibliographical notePublisher Copyright:
© 2021, Springer Nature Switzerland AG.
- Fuzzy classifier