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Gender-Based Analysis of Risk Factors for Dementia Using Senior Cohort

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  • Jaekue Choi

    (Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Future Information Research Center, Korea Institute of Science and Technology Information, Seoul 02456, Korea
    Department of Computer Science and Engineering, Korea University, Seoul 02855, Korea)

  • Lee-Nam Kwon

    (Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Future Information Research Center, Korea Institute of Science and Technology Information, Seoul 02456, Korea)

  • Heuiseok Lim

    (Department of Computer Science and Engineering, Korea University, Seoul 02855, Korea)

  • Hong-Woo Chun

    (Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Future Information Research Center, Korea Institute of Science and Technology Information, Seoul 02456, Korea)

Abstract

Globally, one of the biggest problems with the increase in the elderly population is dementia. However, dementia still has no fundamental cure. Therefore, it is important to predict and prevent dementia early. For early prediction of dementia, it is crucial to find dementia risk factors that increase a person’s risk of developing dementia. In this paper, the subject of dementia risk factor analysis and discovery studies were limited to gender, because it is assumed that the difference in the prevalence of dementia in men and women will lead to differences in the risk factors for dementia among men and women. This study analyzed the Korean National Health Information System—Senior Cohort using machine-learning techniques. By using the machine-learning technique, it was possible to reveal a very small causal relationship between data that are ignored using existing statistical techniques. By using the senior cohort, it was possible to analyze 6000 data that matched the experimental conditions out of 558,147 sample subjects over 14 years. In order to analyze the difference in dementia risk factors between men and women, three machine-learning-based dementia risk factor analysis models were constructed and compared. As a result of the experiment, it was found that the risk factors for dementia in men and women are different. In addition, not only did the results include most of the known dementia risk factors, previously unknown candidates for dementia risk factors were also identified. We hope that our research will be helpful in finding new dementia risk factors.

Suggested Citation

  • Jaekue Choi & Lee-Nam Kwon & Heuiseok Lim & Hong-Woo Chun, 2020. "Gender-Based Analysis of Risk Factors for Dementia Using Senior Cohort," IJERPH, MDPI, vol. 17(19), pages 1-12, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7274-:d:423932
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    References listed on IDEAS

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    1. HeeChel Kim & Hong-Woo Chun & Seonho Kim & Byoung-Youl Coh & Oh-Jin Kwon & Yeong-Ho Moon, 2017. "Longitudinal Study-Based Dementia Prediction for Public Health," IJERPH, MDPI, vol. 14(9), pages 1-16, August.
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    Cited by:

    1. Soo-Jin Lim & Zoonky Lee & Lee-Nam Kwon & Hong-Woo Chun, 2021. "Medical Health Records-Based Mild Cognitive Impairment (MCI) Prediction for Effective Dementia Care," IJERPH, MDPI, vol. 18(17), pages 1-15, September.

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