论文标题
通过使用机器学习：方法和挑战
The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges
论文作者
论文摘要
Covid-19是世界上最大的健康挑战之一。公共卫生政策制定者需要对将来确认的案件的可靠预测来计划医疗设施。机器学习方法从历史数据中学习，并对事件进行预测。机器学习方法已用于预测COVID-19的确认病例的数量。在本文中，我们介绍了这些研究论文的详细回顾。我们提出一种分类学，将它们分为四类。我们进一步提出了这一领域的挑战。我们向机器学习从业人员提供建议，以提高机器学习方法的性能，以预测确认的COVID-19案例。
Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make a prediction about the event. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
