id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-299608-wqa98m4v	Al-Turaiki, Isra	Building predictive models for MERS-CoV infections using data mining techniques	2016-09-15		.txt	text/plain	2378	187	57	title: Building predictive models for MERS-CoV infections using data mining techniques In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections. In healthcare, data mining techniques have been widely applied in different applications including: modeling health outcomes and predicting patient outcomes, evaluation of treatment effectiveness, hospital ranking, and infection control [3] . In this paper, we build several models to predict the stability of the case and the possibility of recovery from MERS-CoV infection. The classification models were built using a dataset of 699 records and 9 attributes and the best accuracy was achieved using decision trees induction algorithms. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV.	./cache/cord-299608-wqa98m4v.txt	./txt/cord-299608-wqa98m4v.txt