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Prediction Model of Influenza Vaccination for Age of 50-64 Adults and the elderly over 65 year old
Journal of the Korean Data & Information Science Society 2022;33:985-908
Published online September 30, 2022;
© 2022 Korean Data and Information Science Society.

Myeung Hee Han1

1School of Nursing, Dongyang University
Correspondence to: 1 Assistant professor, School of Nursing, Dongyang University, Yeongju 36040, Korea. E-mail:
Received June 14, 2022; Revised July 13, 2022; Accepted July 13, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Influenza is an infectious disease that spreads every year, and vaccination is recommended. The Korea Centers for Disease Control and Prevention (KCDC) has suggested that adults aged 50-64 years and the elderly population aged 65 years or older are recommended as subjects with a high incidence of complications during influenza virus infection. The importance of vaccination is being emphasized even more due to COVID-19. This study was conducted to explore the current status of vaccinations and identify factors affecting vaccination through decision tree analysis and logistic regression analysis by using the 2020 community health survey data. From the results of this study for adult group, the vaccination rate was the highest in 65.7% of women who had a final education below high school and were diagnosed with hypertension. For men who were not diagnosed with hypertension or diabetes, the vaccination rate was the lowest at 37.9%. The decision tree prediction model for elderly, those who have female, below high school graduation and diagnosis of hypertension showed the highest rate at 94.2%. In contrast, those who are not diagnosed hypertension, had over university graduation, had the higher the subjective health level showed the lowest inoculation rate of 79.7%. In order to improve the vaccination rate, factors affecting vaccination rate should be identified.
Keywords : Adult, decision trees, elderly, influenza, vaccination.