Analysis of Protein in Diet vs COVID-19 with Machine Learning

Tianzhe Fang, Yuheng Shi, Beining Mu
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引用次数: 1

Abstract

From the first month in 2020, the coronavirus COVID-19 has swept the whole world like a hurricane, which caused a huge number of deaths around the world. There are a lot of different kinds of research on this pandemic. Because the health diet plays an important role in our immune system, which is contributed to the defense of the virus and the recovery after the treatment, this study aims to determine the relationships between the protein quantity in food and COVID-19. To find the correlation between them, we decide to use a basic regression as an analyzing tool to deal with it. To find the relationship between the quantity of protein in diet and COVID-19, we did the investigation first to find a proper dataset and a proper machine learning model. We tried to explore and investigate whether the protein in food is related to the recovery and death rate of COVID-19. By analyzing the data, we found that there is almost no correlation between the two. It seems the quantity of the protein plays a small role in the recuperation of COVID-19, however, there are many other factors, such as other nutritional elements, people’s age, gender, and their underlying disease types, etc., which all affect their recovery status of COVID-19. We need more information for our research and update or change a more proper model for it.
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用机器学习分析饮食中的蛋白质与COVID-19
从2020年的第一个月开始,冠状病毒COVID-19就像飓风一样席卷全球,在全球造成了大量死亡。关于这次大流行有很多不同的研究。由于健康饮食在我们的免疫系统中起着重要作用,有助于防御病毒和治疗后的恢复,因此本研究旨在确定食物中蛋白质含量与COVID-19之间的关系。为了找到它们之间的相关性,我们决定使用基本回归作为分析工具来处理它。为了找到饮食中蛋白质含量与COVID-19之间的关系,我们首先进行了调查,找到了合适的数据集和合适的机器学习模型。我们试图探索和调查食物中的蛋白质是否与COVID-19的恢复和死亡率有关。通过分析数据,我们发现两者之间几乎没有相关性。蛋白质的数量似乎对COVID-19的恢复起着很小的作用,但还有许多其他因素,如其他营养元素、人的年龄、性别、潜在疾病类型等,都影响着他们的COVID-19恢复状况。我们需要更多的信息来研究和更新或改变一个更合适的模型。
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