PDF(Pubmed)
Abstract：
With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .
摘要：
随着2020年COVID-19的爆发，世界各国面临着重大的担忧和挑战。利用人工智能（AI）和数据科学技术进行疾病检测的各种研究已经出现。尽管COVID-19病例有所下降，世界各地仍然有病例和死亡。因此,在症状出现之前早期检测COVID-19对于减少其广泛影响至关重要。幸运的是,智能手表等可穿戴设备已被证明是有价值的生理数据来源，包括心率（HR）和睡眠质量，能够检测炎症性疾病。在这项研究中,我们利用已经存在的数据集,包括个体步数和心率数据,预测症状出现前COVID-19感染的概率.我们训练三个主要的模型架构：梯度提升分类器(GB)、CatBoost树，和TabNet分类器来分析生理数据并比较它们各自的表现。我们还在我们表现最好的模型中添加了一个可解释性层，这澄清了预测结果，并允许对有效性进行详细评估。此外，我们通过从Fitbit设备收集生理数据来创建私有数据集,以保证可靠性并避免偏差.然后使用相同的预训练模型将相同的模型集应用于该私有数据集，并记录了结果。使用基于CatBoost树的方法，我们表现最好的模型在公开数据集上的准确率为85%,优于以往的研究.此外，当应用于私有数据集时，这个相同的预训练CatBoost模型产生了81%的准确率。您可以在链接中找到源代码：https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data。git.
