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• W4313467576 startingPage "40" @default.
• W4313467576 abstract "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%." @default.
• W4313467576 created "2023-01-06" @default.
• W4313467576 creator A5038233087 @default.
• W4313467576 creator A5053635944 @default.
• W4313467576 date "2022-12-21" @default.
• W4313467576 modified "2023-10-18" @default.
• W4313467576 title "Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2" @default.
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• W4313467576 doi "https://doi.org/10.3390/s23010040" @default.
• W4313467576 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36616638" @default.
• W4313467576 hasPublicationYear "2022" @default.
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