Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.

中文翻译：

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使用临床文本数据检测 COVID-19 的基于机器学习的方法。

技术进步对生活的各个领域产生了迅速的影响，无论是医学领域还是任何其他领域。人工智能通过分析数据做出决策，在医疗保健领域显示出可喜的成果。COVID-19 在短时间内影响了 100 多个国家。世界各地的人们在未来都容易受到其后果的影响。开发一种检测冠状病毒的控制系统势在必行。控制当前破坏的解决方案之一是借助各种 AI 工具诊断疾病。在本文中，我们使用经典和集成机器学习算法将文本临床报告分为四类。使用词频/逆文档频率 (TF/IDF)、词袋 (BOW) 和报告长度等技术进行特征工程。这些特征被提供给传统和集成机器学习分类器。逻辑回归和多项朴素贝叶斯显示出比其他 ML 算法更好的结果，测试准确率为 96.2%。将来可以使用循环神经网络来获得更好的准确性。