Longitudinal Study-Based Dementia Prediction for Public Health

Int J Environ Res Public Health. 2017 Aug 30;14(9):983. doi: 10.3390/ijerph14090983.


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: aging; big data; dementia; machine learning; public health; support vector machine.

MeSH terms

  • Databases, Factual
  • Dementia / epidemiology*
  • Forecasting
  • Health Records, Personal
  • Humans
  • Longitudinal Studies
  • Models, Theoretical*
  • National Health Programs
  • Public Health
  • Republic of Korea / epidemiology
  • Support Vector Machine