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

Kwangok Lee,Munkyu Lee,Inseop Na

doi:10.3390/electronics10212668

Kwangok Lee, Munkyu Lee + Show 1 more

Open Access

https://doi.org/10.3390/electronics10212668

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Journal: Electronics	Publication Date: Oct 31, 2021
Citations: 2	License type: CC BY 4.0

Affiliation: Chosun University, Sungkyunkwan University

Abstract

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.

Highlights

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
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)
The experimental results were compared with various machine learning methods through RMSE

Summary

Introduction

As we can see from the spread of COVID-19, SARS, and MERS, we can significantly reduce the number of victims if we can predict the epidemic. The disadvantages of linear regression include: its limitation to linear applications, its unsuitability to many real-life problems, Electronics 2021, 10, 2668 of parameters. The disadvantages of linear regression include: i2tsofli1m6 -. Itation to linear applications, its unsuitability to many real-life problems, the default assumption of input error, and its assumption of independent features may not always be thtreudee. The first step is correlated factor selection for learning for the prediction model. In this correlated factor selection step, we separate irrelevant factors from environmental factors. Preprocess the selected correlated factors and predictions by using LSTMs

Patient information

Findings