id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-330913-8aezw81h	Albahri, A. S.	Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review	2020-05-25		.txt	text/plain	4767	242	49	This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The main contributions of this study are the exploration of the CoV family by reviewing articles on data mining and ML algorithms, the acquisition of a clear understanding of its enhancements, and how previous research has addressed prediction, regression, and classification methods. Given the multidisciplinary topic of this systematic review, data extraction and classification of the selected studies, including data concerning CoV with AI applications (especially ML techniques), were conducted to evaluate the efficacy of this virus in terms of detection, diagnosis and classification throughout AI enhancements. In [8] , a study was conducted in Saudi Arabia between 2013 and 2017 to improve medical diagnosis systems for binary and multiclass problems in MERS-CoV datasets. In [16] , data mining based on statistical methods was utilised to develop a cloud-based medical system with a high prediction accuracy to prevent MERS-CoV spread within different regions.	./cache/cord-330913-8aezw81h.txt	./txt/cord-330913-8aezw81h.txt