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Longitudinal Study-Based Dementia Prediction for Public Health

Author

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  • HeeChel Kim

    (Science and Technology Management Policy, University of Science & Technology, Daejeon 34113, Korea
    Korea Institute of Science and Technology Information, Seoul 02456, Korea)

  • Hong-Woo Chun

    (Korea Institute of Science and Technology Information, Seoul 02456, Korea
    Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
    Science and Technology Information Science, University of Science & Technology, Daejeon 34113, Korea)

  • Seonho Kim

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

  • Byoung-Youl Coh

    (Korea Institute of Science and Technology Information, Seoul 02456, Korea)

  • Oh-Jin Kwon

    (Korea Institute of Science and Technology Information, Seoul 02456, Korea
    Science and Technology Information Science, University of Science & Technology, Daejeon 34113, Korea)

  • Yeong-Ho Moon

    (Korea Institute of Science and Technology Information, Seoul 02456, Korea
    Science and Technology Information Science, University of Science & Technology, Daejeon 34113, Korea)

Abstract

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:9:p:983-:d:110343
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    References listed on IDEAS

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    Cited by:

    1. Seonho Kim & Jungjoon Kim & Hong-Woo Chun, 2018. "Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease," IJERPH, MDPI, vol. 15(8), pages 1-21, August.
    2. 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.
    3. 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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