- 권한신청
- E-ISSN2233-5382
- KCI
A Study on the Prediction Model of the Elderly Depression
A Study on the Prediction Model of the Elderly Depression
SUH, Eung-Kyo (Graduate School of Business, Dankook University)
KIM, Tae-Hyeong (Department of Data Knowledge Service Engineering, Graduate School. Dankook University)
Abstract
Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.
- keywords
- Machine Learning, Classification, Elderly Depresiion, Random Forest, Decision Tree
참고문헌
Bae, S. W. (2019). A Comparative Study on the Prediction of Housing Price Using Machine Learning (Doctoral dissertation). Dankook University, Yongin, Korea.
Bae, S. W., & Shin W. S. (2005). The Center for Epidemiological Studies-Depression Scale (CES-D): Application of Verification Factor Analysis Method. Health and Social Sciences, 18, 166.
Breiman, L. (2001). Random forests, Machine Learning, 45, 5.
Cho, K. S., & Ye, W. J. (2014). P2P Traffic Classification using Advanced Heuristic Rules and Analysis of Decision Tree Algorithms. Journal of The Korea Society of Computer and Information, 19(3), 45-54.
Choi, J. H., & Seo, D. S. (1999). Application of Data Mining Decision Tree. Statistical Analysis Study, 4(1), 62.
Choi, P. S., & Man, I. S. (2018). A Model for the Employment Prediction of College Graduates Using Machine Learning Technique. Job Competency Development Research, 21(1), 35-36.
COGNUB (2019). Cognitive Computing and Machine Learning. Retrieved May 14, 2020, from http://www.cognub.com/index.php/cognitive-platform/
Trevor, H., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning. The element of statistical learning (pp.305-585). New York, NY: Springer.
Health Insurance Review & Assessment Service (2019). Statistic of National Concerned Diseases-Depression. Retrieved May 12, 2020, from http://opendata.hira.or.kr/op/opc/olapMfrnIntrsIlnsInfo.do
Heo, M. S., Park, B. S., & Bae, S. W. (2015). Verification of measurement invariant of the abbreviated CES-D scale in Korean. Mental Health and Social Welfare, 43(2), 314.
Jeong, I. Y. (2015). A Study on the Causal Model of Preparing for the Elderly and Suicide of Middle and Middle-aged People: Focused on the Intermediary Effects of Social Participation, Social Support, Stress, and Depression (Doctoral dissertation). Catholic University, Bucheon, Korea.
Myers, J. K., Morton, K., Robins, L. N., George, L. K., Karno, M., & Locke, B. Z. (1988). One-Month Prevalence of Mental Disorders in the United States. Arch Gen Psychiatry, 45, 977- 986.
KEIS (2016). The 6th Basic Survey of the Aging Research Panel (KLoSA) in 2016.
KEIS (2019). 2018 Aging Research Panel User Guide.
Kim, B. J. (2020). Factors Influencing Depressive Symptoms in the Elderly: Using the 7th Korea National Health and Nutrition Examination Survey. Journal of Health Informatics and Statistics, 45(2), 165-172.
Kim, D. H. (2009). A Study on the Relationship between Family Support, Self-respect, and Depression in which Older People are Late. Elderly Welfare Research, 13, 136, 138.
Kim, D. J., Cho, S. Y., Choi, J. S., Lee, M. W., Kang, S. H. & Kim, S. W., (2019). A Study on the Relationship between cognitive function and senile depression and senile stress. Journal of the Korean Society of Clinical Examination and Sciences, 51(1), 111.
Kim, I. J. (2014). Deep Learning: New Trends in Machine Learning. Journal of the Korean Telecommunications Society, 31(11), 52.
Kim, J. K., Lee, K. B., & Hong, S. K. (2017). ECG-based biometric authentication using random forest. Journal of Electronic Engineering Society, 54(6), 102.
Kim, J. W. (2017). The Effects of Economic Stabilization and Retirement Income Security Policy on Mental Health (Doctoral dissertation). Seoul National University, Seoul, Korea.
Ko, K. D. (2012). The Relationship between Health Risk Behavior and Mental Health in the Elderly in Korea: Korean Longitudinal Study of Ageing(KLoSA). Journal of the Korean Geriatrics Society, 16(2), 66-73.
Kim, S. J., Ahn, S. J., & Ahn, H. C. (2016). Application of Random Forest to Predict Corporate Credit Ratings. Industrial Innovation Research, 32(1), 187-191.
Kim, T. H., Lee, Y. T., Hwang, E. P., & Won, J. M. (2008). A Study on the Establishment of Subway Station Area in New Town Area Using CART Analysis. Journal of the Korean Railway Society, 11(3), 217.
Mitchell, M. (1997). Machine Learning. New York, NY: McGraw- Hill.
Na, D. Y. (2019). A Study on the Smart Farm Technology for Animal Welfare based on IoT using Machine Learning Algorithm (Doctoral dissertation). Konkuk University, Seoul, Korea.
Irwin, M. R., Artin, K. H., & Oxman, M. N. (1999). Screening for Depression in the Older Adult: Criterion Validity of the 10Item Center for Epidemiological Studies Depression Scale (CES-D). Archives of Internal Medicine, 159(15), 1701-1704.
Ministry of Health and Welfare. (2006). Epidemiological Survey on the Actual Condition of Mental Diseases (pp.83).
Park, J. H., & Kim, K. W. (2011). A Study on the Epidemiology of Depression in Korea. Journal of the Korean Medical Association, 54(4), 362.
Russell, D., & Taylor, J. (2009). Living alone and depressive symptoms: The influence of gender, physical disability, and social support among Hispanic and non-Hispanic older adults. Journal of Gerontology Series B: Psychological Science and Social Sciences, 64(1), 95-104.
Yoo, H. S., Suh, E. K., & Kim, T. H. (2020). A Study on Technology Acceptance of Elderly living Alone in Smart City Environment: Based on AI Speaker. Journal of Industrial Distribution & Business, 11(2), 41-48.
Yoo, J. E. (2015). Random forests, an alternative data mining technique to decision tree. Journal of Educational Evaluation, 28(2), 433-434.
- 다운로드 수
- 조회수
- 0KCI 피인용수
- 0WOS 피인용수