4.7 Article

Development of Keyword Trend Prediction Models for Obesity Before and After the COVID-19 Pandemic Using RNN and LSTM: Analyzing the News Big Data of South Korea

期刊

FRONTIERS IN PUBLIC HEALTH
卷 10, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fpubh.2022.894266

关键词

obesity; COVID-19 pandemic; text mining; topic modeling analysis; LSTM

资金

  1. Basic Science Research Program through National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2018R1D1A1B07041091, 2021S1A5A8062526]
  2. Development of Open-Lab based on 4P in the Southeast Zone
  3. National Research Foundation of Korea [2021S1A5A8062526] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study examined the changes in keywords and topics related to obesity in South Korean society before and after the COVID-19 pandemic. The results showed that obesity increased significantly after the outbreak of COVID-19 and was associated with higher risks and severity of infection. The study also developed models to predict the timing of obesity trends before and after the pandemic.
The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (>= 19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underweight. Therefore, identifying the difference in potential factors for obesity before and after the pandemic is an important issue in health science. This study identified the keywords and topics that were formed before and after the COVID-19 pandemic in the South Korean society and how they had been changing by conducting a web crawling of South Korea's news big data using obesity as a keyword. This study also developed models for predicting timing before and after the COVID-19 pandemic using keywords. Topic modeling results was found that the trend of keywords was different between before the COVID-19 pandemic and after the COVID-19 pandemic: topics such as degenerative arthritis, diet, and side effects of diet treatment were derived before the COVID-19 pandemic, while topics such as COVID blues and relationship between dietary behavior and disease were confirmed after the COVID-19 pandemic. This study also showed that both RNN and LSTM had high accuracy (over 97%), but the accuracy of the RNN model (98.22%) had higher than that of the LSTM model (97.12%) by 0.24%. Based on the results of this study, it will be necessary to continuously pay attention to the newly added obesity-related factors after the COVID-19 pandemic and to prepare countermeasures at the social level based on the results of this study.

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