Background and objective

The novel coronavirus disease 2019 (COVID-19) is considered a pandemic by the World Health Organization (WHO). As of April 3, 2020, there were 1,009,625 reported confirmed cases, and 51,737 reported deaths. Doctors have been faced with a myriad of patients who present with many different symptoms. This raises two important questions. What are the common symptoms, and what are their relative importance?

Methods

A non-structured and incomplete COVID-19 dataset of 14,251 confirmed cases was preprocessed. This produced a complete and organized COVID-19 dataset of 738 confirmed cases. Six different feature selection algorithms were then applied to this new dataset. Five of these algorithms have been proposed earlier in the literature. The sixth is a novel algorithm being proposed by the authors, called Variance Based Feature Weighting (VBFW), which not only ranks the symptoms (based on their importance) but also assigns a quantitative importance measure to each symptom.

Results

For our COVID-19 dataset, the five different feature selection algorithms provided different rankings for the most important top-five symptoms. They even selected different symptoms for inclusion within the top five. This is because each of the five algorithms ranks the symptoms based on different data characteristics. Each of these algorithms has advantages and disadvantages. However, when all these five rankings were aggregated (using two different aggregating methods) they produced two identical rankings of the five most important COVID-19 symptoms. Starting from the most important to least important, they were: Fever/Cough, Fatigue, Sore Throat, and Shortness of Breath. (Fever and cough were ranked equally in both aggregations.) Meanwhile, the sixth novel Variance Based Feature Weighting algorithm, chose the same top five symptoms, but ranked fever much higher than cough, based on its quantitative importance measures for each of those symptoms (Fever - 75 %, Cough - 39.8 %, Fatigue - 16.5 %, Sore Throat - 10.8 %, and Shortness of Breath - 6.6 %). Moreover, the proposed VBFW method achieved an accuracy of 92.1 % when used to build a one-class SVM model, and an NDCG@5 of 100 %.

Conclusions

Based on the dataset, and the feature selection algorithms employed here, symptoms of Fever, Cough, Fatigue, Sore Throat and Shortness of Breath are important symptoms of COVID-19. The VBFW algorithm also indicates that Fever and Cough symptoms were especially indicative of COVID-19, for the confirmed cases that are documented in our database.

中文翻译：

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一种为 COVID-19 患者的症状分配重要性权重的新计算方法

背景和目标

新型冠状病毒病 2019 (COVID-19) 被世界卫生组织 (WHO) 视为大流行病。截至2020年4月3日，报告确诊病例1009625例，报告死亡51737例。医生们遇到过无数具有许多不同症状的患者。这就提出了两个重要的问题。常见症状是什么，它们的相对重要性是什么？

方法

对包含 14,251 例确诊病例的非结构化和不完整的 COVID-19 数据集进行了预处理。这产生了一个完整且有组织的 COVID-19 数据集，其中包含 738 例确诊病例。然后将六种不同的特征选择算法应用于这个新数据集。其中五种算法已在文献中较早提出。第六个是作者提出的一种新算法，称为基于方差的特征加权 (VBFW)，它不仅对症状进行排名（基于它们的重要性），而且还为每个症状分配一个量化的重要性度量。

结果

对于我们的 COVID-19 数据集，五种不同的特征选择算法为最重要的前五种症状提供了不同的排名。他们甚至选择了不同的症状来列入前五名。这是因为五种算法中的每一种都根据不同的数据特​​征对症状进行排序。这些算法中的每一个都有优点和缺点。然而，当对所有这五个排名进行汇总时（使用两种不同的汇总方法），他们对五个最重要的 COVID-19 症状产生了两个相同的排名。从最重要到最不重要，它们是：发烧/咳嗽、疲劳、喉咙痛和呼吸急促。（发烧和咳嗽排名相同在两个聚合中。）与此同时，第六种新颖的基于方差的特征加权算法选择了相同的前五种症状，但根据其对每种症状的量化重要性度量（发烧 - 75％，咳嗽 - 39.8% ，疲劳 - 16.5% ，喉咙痛 - 10.8% ，呼吸急促 - 6.6%）。此外，所提出的 VBFW 方法在用于构建单类 SVM 模型时达到了 92.1% 的准确率，NDCG@5 达到了 100%。

结论

根据数据集和此处使用的特征选择算法，发烧、咳嗽、疲劳、喉咙痛和呼吸急促等症状是 COVID-19 的重要症状。VBFW 算法还表明，对于我们数据库中记录的确诊病例，发烧和咳嗽症状特别指示 COVID-19。