ArticleSensors (Basel, Switzerland)2022
Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Predicting SARS-CoV-2-specific CD4Clinical and experimental medicine · 2025Article
- A machine learning model predicts stroke associated with blood cadmium level.Scientific reports · 2024Article
- Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods.International journal of environmental research and public health · 2023Article
- SARS-CoV-2 Surveillance Through China Influenza Surveillance Information System - China, December 1, 2022 to February 12, 2023.China CDC weekly · 2023Article
- Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models.Sensors (Basel, Switzerland) · 2023Article
- Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.