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Volume 14
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Article

Longitudinal Study-Based Dementia Prediction for Public Health

by
HeeChel Kim
1,2,
Hong-Woo Chun
2,3,4,*,
Seonho Kim
2,3,
Byoung-Youl Coh
2,
Oh-Jin Kwon
2,4 and
Yeong-Ho Moon
2,4,*
1
Science and Technology Management Policy, University of Science & Technology, Daejeon 34113, Korea
2
Korea Institute of Science and Technology Information, Seoul 02456, Korea
3
Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
4
Science and Technology Information Science, University of Science & Technology, Daejeon 34113, Korea
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(9), 983; https://doi.org/10.3390/ijerph14090983
Submission received: 6 July 2017 / Revised: 21 August 2017 / Accepted: 28 August 2017 / Published: 30 August 2017
(This article belongs to the Special Issue Innovations in Biostatistical Methods and Data for Public Health Research)
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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.
Keywords: public health; aging; dementia; big data; machine learning; support vector machine public health; aging; dementia; big data; machine learning; support vector machine

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MDPI and ACS Style

Kim, H.; Chun, H.-W.; Kim, S.; Coh, B.-Y.; Kwon, O.-J.; Moon, Y.-H. Longitudinal Study-Based Dementia Prediction for Public Health. Int. J. Environ. Res. Public Health 2017, 14, 983. https://doi.org/10.3390/ijerph14090983

AMA Style

Kim H, Chun H-W, Kim S, Coh B-Y, Kwon O-J, Moon Y-H. Longitudinal Study-Based Dementia Prediction for Public Health. International Journal of Environmental Research and Public Health. 2017; 14(9):983. https://doi.org/10.3390/ijerph14090983

Chicago/Turabian Style

Kim, HeeChel, Hong-Woo Chun, Seonho Kim, Byoung-Youl Coh, Oh-Jin Kwon, and Yeong-Ho Moon. 2017. "Longitudinal Study-Based Dementia Prediction for Public Health" International Journal of Environmental Research and Public Health 14, no. 9: 983. https://doi.org/10.3390/ijerph14090983

APA Style

Kim, H., Chun, H.-W., Kim, S., Coh, B.-Y., Kwon, O.-J., & Moon, Y.-H. (2017). Longitudinal Study-Based Dementia Prediction for Public Health. International Journal of Environmental Research and Public Health, 14(9), 983. https://doi.org/10.3390/ijerph14090983

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