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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/21/2668 |
| _version_ | 1797512618051633152 |
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| author | Kwangok Lee Munkyu Lee Inseop Na |
| author_facet | Kwangok Lee Munkyu Lee Inseop Na |
| author_sort | Kwangok Lee |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:04:20Z |
| format | Article |
| id | doaj.art-1fad9524f84244508aabae9d1498304c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T06:04:20Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1fad9524f84244508aabae9d1498304c2023-11-22T20:39:05ZengMDPI AGElectronics2079-92922021-10-011021266810.3390/electronics10212668Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in KoreaKwangok Lee0Munkyu Lee1Inseop Na2National Program of Excellence in Software Centre, Chosun University, Gwangju 61452, KoreaDepartment of Electronic and Electrical Engineering, SungKyunKwan University, Suwon 16419, KoreaNational Program of Excellence in Software Centre, Chosun University, Gwangju 61452, KoreaIn 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.https://www.mdpi.com/2079-9292/10/21/2668predictingregional outbreakshepatitis Adeep learningopen databig data |
| spellingShingle | Kwangok Lee Munkyu Lee Inseop Na Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea Electronics predicting regional outbreaks hepatitis A deep learning open data big data |
| title | 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_short | 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 |
| topic | predicting regional outbreaks hepatitis A deep learning open data big data |
| url | https://www.mdpi.com/2079-9292/10/21/2668 |
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