id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-326831-dvg0isgt	Muhammad, L. J.	Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery	2020-06-21		.txt	text/plain	2707	145	52	The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor. Data mining algorithm which includes decision tree, support vector machine, naive Bayes, logistic regression random forest, and K-nearest neighbor were applied directly on the dataset using python programming language to develop the models.	./cache/cord-326831-dvg0isgt.txt	./txt/cord-326831-dvg0isgt.txt