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- Title
Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach.
- Authors
Dobrijević, Dejan; Antić, Jelena; Rakić, Goran; Katanić, Jasmina; Andrijević, Ljiljana; Pastor, Kristian
- Abstract
Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.
- Subjects
SERBIA; INFLUENZA diagnosis; INFERENTIAL statistics; STATISTICS; SUPPORT vector machines; COVID-19; PREDICTIVE tests; CROSS-sectional method; MACHINE learning; DIFFERENTIAL diagnosis; PEDIATRICS; MANN Whitney U Test; RANDOM forest algorithms; T-test (Statistics); DESCRIPTIVE statistics; DATA analysis software; DATA analysis; ALGORITHMS; CHILDREN
- Publication
Children, 2023, Vol 10, Issue 5, p761
- ISSN
2227-9067
- Publication type
Article
- DOI
10.3390/children10050761