AI Article Synopsis
- A machine learning decision support system was developed to improve COVID-19 diagnostics by utilizing routine lab parameters, aiming to reduce misdiagnoses and testing costs.
- The study involved reviewing patient files, focusing on demographic, CT, and lab data from both asymptomatic individuals with negative and positive RT-PCR tests.
- The proposed system demonstrated high accuracy rates in detecting COVID-19, achieving 97.56% through CT image classification using a specific hybrid method (AlexNet-SVM) and 97.86% when incorporating laboratory data.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015244 | PMC |
| http://dx.doi.org/10.1002/ima.22705 | DOI Listing |
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