id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-252894-c02v47jz	Chae, Sangwon	Predicting Infectious Disease Using Deep Learning and Big Data	2018-07-27		.txt	text/plain	10663	605	57	This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. Therefore, the aim of this study is to design a model that uses the infectious disease occurrence data provided by the KCDC, search query data from search engines that are specialized for South Korea, Twitter social media big data, and weather data such as temperature and humidity. Figure 1 shows the overall framework of the model used in this study including the data collection process and the comparison of models designed using the deep neural network (DNN) method, the long-short term memory (LSTM) method, the autoregressive integrated moving average (ARIMA) method, and the ordinary least squares (OLS) method.	./cache/cord-252894-c02v47jz.txt	./txt/cord-252894-c02v47jz.txt