A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting tens of millions people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models, random forest, and multinomial logistic regression to predict isolation, release, and decease states for COVID-19 patients in South Korea. The prediction can help health providers and decision makers to distinguish the states of infected patients based on their features in early intervention to take an action either by releasing or isolating the patient after the infection. The proposed approaches are evaluated using Data Science for COVID-19 (DS4C) dataset. An analysis of DS4C dataset is also provided. Experimental results and evaluation show that multinomial logistic regression outperforms other approaches with 95% in a state prediction accuracy and a weighted average F1-score of 95%.

如今，呼吸系统综合症 COVID-19 大流行已成为一个严重的公共卫生问题。COVID-19 病毒已影响全球数以千万计的人。他们中的一些人已经康复并被释放。其他人已被隔离，很少有人不幸去世。在本文中，我们应用并比较了不同的机器学习方法，例如决策树模型、随机森林和多项逻辑回归，以预测韩国 COVID-19 患者的隔离、释放和死亡状态。该预测可以帮助卫生提供者和决策者根据感染患者的早期干预特征来区分他们的状态，从而在感染后通过释放或隔离患者来采取行动。使用 COVID-19 (DS4C) 数据集的数据科学对所提出的方法进行了评估。还提供了对 DS4C 数据集的分析。实验结果和评估表明，多项逻辑回归以 95% 的状态预测准确率和 95% 的加权平均 F1 分数优于其他方法。