Longitudinal Study-Based Dementia Prediction for Public Health
Science and Technology Management Policy, University of Science & Technology, Daejeon 34113, Korea
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
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(9), 983; https://doi.org/10.3390/ijerph14090983
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)
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.