Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.
Sensors (Basel). 2022 Dec 21;23(1):40. doi: 10.3390/s23010040.
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%.
新型冠状病毒肺炎（COVID-19）疫情由 SARS-CoV-2 病毒引起，始于 2019 年 12 月。该病毒最初在中国武汉地区被报告。这是一种新型冠状病毒，此前从未在人类中分离出来。在严重的情况下，可能会发生肺炎、急性呼吸窘迫综合征、多器官衰竭甚至死亡。现在，疫苗、抗病毒药物和适当的治疗方法的存在是对抗这种疾病的盟友。在目前的研究工作中，我们利用有监督的机器学习（ML）模型来确定 SARS-CoV-2 发生的早期症状。为此，我们尝试了几种 ML 模型，结果表明，集成模型，即堆叠，表现优于其他模型，达到了 90.9%的准确率、90.9%的精度、90.9%的召回率和 90.9%的 F 值，以及 96.4%的曲线下面积（AUC）。
