A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique.
Diagnostics (Basel)
; 13(11)2023 May 28.
Article
in English
| MEDLINE | ID: covidwho-20232015
ABSTRACT
COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
Language:
English
Year:
2023
Document Type:
Article
Affiliation country:
Diagnostics13111886
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