Can laboratory parameters be an alternative to CT and RT‐PCR in the diagnosis of COVID‐19? A machine learning approach

Abstract

In this study, a machine learning‐based decision support system that uses routine laboratory parameters has been proposed in order to increase the diagnostic success in COVID‐19. The main goal of the proposed method was to reduce the number of misdiagnoses in the RT‐PCR and CT scans and to reduce the cost of testing. In this study, we retrospectively reviewed the files of patients who presented to the coronavirus outpatient. The demographic, thoracic CT, and laboratory data of the individuals without any symptoms of the disease, who had negative RT‐PCR test and who had positive RT‐PCR test were analyzed. CT images were classified using hybrid CNN methods to show the superiority of the decision support system using laboratory parameters. Detection of COVID‐19 from CT images achieved an accuracy of 97.56% with the AlexNet‐SVM hybrid method, while COVID‐19 was classified with an accuracy of 97.86% with the proposed method using laboratory parameters.

Fighting against COVID‐19: Innovations and applications

“…Kalaycı. et al 58 propose an ML‐based DSS, which employs routine laboratory parameters to increase diagnostic success in COVID‐19. Based on acoustic cough features, Sunitha et al 59 have conducted a comparative examination of DNN architectures for the dynamic diagnosis of COVID‐19.…”