id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-354627-y07w2f43	pinter, g.	COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach	2020-05-06		.txt	text/plain	5478	337	50	As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. Due to the complex nature of the COVID-19 outbreak and its irregularity in different countries, the standard epidemiological models, i.e., susceptible-infected-resistant (SIR)-based models, had been challenged for delivering higher performance in individual nations. In this study the hybrid machine learning model of MLP-ICA and ANFIS are used to predict the COVID-19 outbreak in Hungary. Both machine learning models, as an alternative to epidemiological models, showed potential in predicting COVID-19 outbreak as well as estimating total mortality.	./cache/cord-354627-y07w2f43.txt	./txt/cord-354627-y07w2f43.txt