Globally, one of the biggest problems with the increase in the elderly population is dementia. However, dementia still has no fundamental cure. Therefore, it is important to predict and prevent dementia early. For early prediction of dementia, it is crucial to find dementia risk factors that increase a person’s risk of developing dementia. In this paper, the subject of dementia risk factor analysis and discovery studies were limited to gender, because it is assumed that the difference in the prevalence of dementia in men and women will lead to differences in the risk factors for dementia among men and women. This study analyzed the Korean National Health Information System—Senior Cohort using machine-learning techniques. By using the machine-learning technique, it was possible to reveal a very small causal relationship between data that are ignored using existing statistical techniques. By using the senior cohort, it was possible to analyze 6000 data that matched the experimental conditions out of 558,147 sample subjects over 14 years. In order to analyze the difference in dementia risk factors between men and women, three machine-learning-based dementia risk factor analysis models were constructed and compared. As a result of the experiment, it was found that the risk factors for dementia in men and women are different. In addition, not only did the results include most of the known dementia risk factors, previously unknown candidates for dementia risk factors were also identified. We hope that our research will be helpful in finding new dementia risk factors.
|Number of pages||12|
|Journal||International journal of environmental research and public health|
|Publication status||Published - 2020 Oct 1|
Bibliographical noteFunding Information:
Funding: This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST).
This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST). This study used KNHIS-SC data (NHIS-2016-2-247) made by the Korea National Health Insurance Service (KNHIS).
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Deep learning
- Dementia risk factor
- Machine learning
- Senior cohort
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis