Longitudinal Study-Based Dementia Prediction for Public Health

• Published in: International journal of environmental research and public health Vol. 14; no. 9; p. 983
• Main Authors: Kim, HeeChel, Chun, Hong-Woo, Kim, Seonho, Coh, Byoung-Youl, Kwon, Oh-Jin, Moon, Yeong-Ho
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.08.2017; MDPI
• Subjects: Aging; Artificial intelligence; Big Data; Biomedical data; Cognitive ability; Correlation analysis; Databases, Factual; Dementia; Dementia - epidemiology; Dementia disorders; Family medical history; Forecasting; GDP; Gross Domestic Product; Health care; Health insurance; Health Records, Personal; Humans; Learning algorithms; Lifestyles; Longitudinal Studies; Medical records; Models, Theoretical; National Health Programs; Older people; Personal health; Population; Public Health; Republic of Korea - epidemiology; Researchers; Science; Social conditions; Social networks; Sociodemographics; Studies; Support Vector Machine; United Kingdom > UK; France; United States > US; Switzerland; dementia; support vector machine; aging; big data; machine learning; public health
• ISSN: 1660-4601; 1661-7827; 1660-4601
• 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.