巴西患者Covid19数据集的数据采掘分析
Data Mining Approach to Analyze Covid19 Dataset of Brazilian Patients
摘要：
疫情源自冠状病毒（COVID-19），该名称由世界卫生组织于二零二零年首月起。事实上，几乎所有国家都出现了covid 19阳性病例，政府正在选择不同的卫生政策来阻止感染，许多研究小组正在研究患者数据以了解病毒，同时科学家正在寻找一个真空来加强免疫系统以应对covid 19病毒。感染人数最多的国家之一是巴西，截至8月11日，共有3，112，393例。圣保罗州研究基金会（Fapesp）发布了一个数据集，这是与医院（爱因斯坦，Libanes），实验室（Fleury）和圣保罗大学合作的创新，以促进对这一趋势主题的研究。本文提出了一个探索性的分析数据集，使用数据采掘的方法，并发现一些不一致的，即NaN值，空参考值的分析物，离群值的分析物的结果，编码问题。这些结果是经过清理的数据集，用于未来的研究，但由于非数值、空值和超出参考范围的数值，至少有20%的数据被丢弃。
英文摘要：
The pandemic originated by coronavirus(covid-19), name coined by World Health Organization during the first month in 2020. Actually, almost all the countries presented covid19 positive cases and governments are choosing different health policies to stop the infection and many research groups are working on patients data to understand the virus, at the same time scientists are looking for a vacuum to enhance imnulogy system to tack covid19 virus. One of top countries with more infections is Brazil, until August 11 had a total of 3,112,393 cases. Research Foundation of Sao Paulo State(Fapesp) released a dataset, it was an innovative in collaboration with hospitals(Einstein, Sirio-Libanes), laboratory(Fleury) and Sao Paulo University to foster reseach on this trend topic. The present paper presents an exploratory analysis of the datasets, using a Data Mining Approach, and some inconsistencies are found, i.e. NaN values, null references values for analytes, outliers on results of analytes, encoding issues. The results were cleaned datasets for future studies, but at least a 20\% of data were discarded because of non numerical, null values and numbers out of reference range.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
