Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea

In 2020 and 2021, humanity lived in fear due to the COVID-19 pandemic. However, with the development of artificial intelligence technology, mankind is attempting to tackle many challenges from currently unpredictable epidemics. Korean society has been exposed to various infectious diseases since the...

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Autores principales:	Kwangok Lee, Munkyu Lee, Inseop Na
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	predicting regional outbreaks hepatitis A deep learning open data big data Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/1fad9524f84244508aabae9d1498304c
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id	oai:doaj.org-article:1fad9524f84244508aabae9d1498304c
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spelling	oai:doaj.org-article:1fad9524f84244508aabae9d1498304c2021-11-11T15:39:43ZPredicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea10.3390/electronics102126682079-9292https://doaj.org/article/1fad9524f84244508aabae9d1498304c2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2668https://doaj.org/toc/2079-9292In 2020 and 2021, humanity lived in fear due to the COVID-19 pandemic. However, with the development of artificial intelligence technology, mankind is attempting to tackle many challenges from currently unpredictable epidemics. Korean society has been exposed to various infectious diseases since the Korean War in 1950, and to overcome them, the six most serious cases in National Notifiable Infectious Diseases (NNIDs) category I were defined. Although most infectious diseases have been overcome, viral hepatitis A has been on the rise in Korean society since 2010. Therefore, in this paper, the prediction of viral hepatitis A, which is rapidly spreading in Korean society, was predicted by region using the deep learning technique and a publicly available dataset. For this study, we gathered information from five organizations based on the open data policy: Korea Centers for Disease Control and Prevention (KCDC), National Institute of Environmental Research (NIER), Korea Meteorological Agency (KMA), Public Open Data Portal, and Korea Environment Corporation (KECO). Patient information, water environment information, weather information, population information, and air pollution information were acquired and correlations were identified. Next, an epidemic outbreak prediction was performed using data preprocessing and 3D LSTM. The experimental results were compared with various machine learning methods through RMSE. In this paper, we attempted to predict regional epidemic outbreaks of hepatitis A by linking the open data environment with deep learning. It is expected that the experimental process and results will be used to present the importance and usefulness of establishing an open data environment.Kwangok LeeMunkyu LeeInseop NaMDPI AGarticlepredictingregional outbreakshepatitis Adeep learningopen databig dataElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2668, p 2668 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	predicting regional outbreaks hepatitis A deep learning open data big data Electronics TK7800-8360
spellingShingle	predicting regional outbreaks hepatitis A deep learning open data big data Electronics TK7800-8360 Kwangok Lee Munkyu Lee Inseop Na Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea
description	In 2020 and 2021, humanity lived in fear due to the COVID-19 pandemic. However, with the development of artificial intelligence technology, mankind is attempting to tackle many challenges from currently unpredictable epidemics. Korean society has been exposed to various infectious diseases since the Korean War in 1950, and to overcome them, the six most serious cases in National Notifiable Infectious Diseases (NNIDs) category I were defined. Although most infectious diseases have been overcome, viral hepatitis A has been on the rise in Korean society since 2010. Therefore, in this paper, the prediction of viral hepatitis A, which is rapidly spreading in Korean society, was predicted by region using the deep learning technique and a publicly available dataset. For this study, we gathered information from five organizations based on the open data policy: Korea Centers for Disease Control and Prevention (KCDC), National Institute of Environmental Research (NIER), Korea Meteorological Agency (KMA), Public Open Data Portal, and Korea Environment Corporation (KECO). Patient information, water environment information, weather information, population information, and air pollution information were acquired and correlations were identified. Next, an epidemic outbreak prediction was performed using data preprocessing and 3D LSTM. The experimental results were compared with various machine learning methods through RMSE. In this paper, we attempted to predict regional epidemic outbreaks of hepatitis A by linking the open data environment with deep learning. It is expected that the experimental process and results will be used to present the importance and usefulness of establishing an open data environment.
format	article
author	Kwangok Lee Munkyu Lee Inseop Na
author_facet	Kwangok Lee Munkyu Lee Inseop Na
author_sort	Kwangok Lee
title	Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea
title_short	Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea
title_full	Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea
title_fullStr	Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea
title_full_unstemmed	Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea
title_sort	predicting regional outbreaks of hepatitis a using 3d lstm and open data in korea
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1fad9524f84244508aabae9d1498304c
work_keys_str_mv	AT kwangoklee predictingregionaloutbreaksofhepatitisausing3dlstmandopendatainkorea AT munkyulee predictingregionaloutbreaksofhepatitisausing3dlstmandopendatainkorea AT inseopna predictingregionaloutbreaksofhepatitisausing3dlstmandopendatainkorea
_version_	1718434524536242176

Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea

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