Detection of SARS-CoV-2 from raman spectroscopy data using machine learning models
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the coronaviruses that caused the COVID-19 pandemic. The pathogenic SARS-CoV-2 virus can act as a miRNA sponge to lower cellular miRNA levels, making it a more dangerous human coronavirus. Diagnostic testing of the virus is intended to identify current infection in individuals and is performed when a person exhibits symptoms that are compatible with COVID-19. In this work, machine learning models (artificial neural network, decision tree, and support vector machine) are used to classify Raman spectroscopy samples as healthy or infected with SARS-CoV-2. The aim of the work is to introduce an alternative method for detecting SARS-CoV-2. The accuracy of the artificial neural network, the support vector machine and the decision tree were 94%, 90%, and 87%, respectively. The algorithms produced evidence of high recall and specificity. Hence, integrating Raman spectroscopy with machine learning has the potential to serve as an alternative diagnostic tool..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:388, p 07002 |
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| Enthalten in: |
MATEC Web of Conferences - 388, p 07002(2023) |
| Sprache: |
Englisch ; Französisch |
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| Beteiligte Personen: |
Tsebesebe Nkgaphe [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
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| doi: |
10.1051/matecconf/202338807002 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ096358343 |
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