Cargando…
Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2
The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distr...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824026/ https://www.ncbi.nlm.nih.gov/pubmed/36616638 http://dx.doi.org/10.3390/s23010040 |
_version_ | 1784866307563323392 |
---|---|
author | Dritsas, Elias Trigka, Maria |
author_facet | Dritsas, Elias Trigka, Maria |
author_sort | Dritsas, Elias |
collection | PubMed |
description | The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%. |
format | Online Article Text |
id | pubmed-9824026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98240262023-01-08 Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 Dritsas, Elias Trigka, Maria Sensors (Basel) Article The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%. MDPI 2022-12-21 /pmc/articles/PMC9824026/ /pubmed/36616638 http://dx.doi.org/10.3390/s23010040 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dritsas, Elias Trigka, Maria Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 |
title | Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 |
title_full | Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 |
title_fullStr | Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 |
title_full_unstemmed | Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 |
title_short | Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2 |
title_sort | supervised machine learning models to identify early-stage symptoms of sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824026/ https://www.ncbi.nlm.nih.gov/pubmed/36616638 http://dx.doi.org/10.3390/s23010040 |
work_keys_str_mv | AT dritsaselias supervisedmachinelearningmodelstoidentifyearlystagesymptomsofsarscov2 AT trigkamaria supervisedmachinelearningmodelstoidentifyearlystagesymptomsofsarscov2 |