Original Article

Can the Random Forests Model Improve the Power to Predict the Inten-tion of the Elderly in a Community to Participate in a Cognitive Health Promotion Program?


Background: We aimed to develop a model predicting the participation of the elderly in a cognitive health program using the random forest algorithm and presented baseline information for enhancing cognitive health.

Methods: This study analyzed the raw data of Seoul Welfare Panel Study (SWPS) (20), which was surveyed by Seoul Welfare Foundation for the residents of Seoul from Jun 1st to Aug 31st, 2015. Subjects were 2,111 (879 men and 1232 women) persons aged 60 yr and older living in the community who were not diagnosed with dementia. The outcome variable was the intention to participate in a cognitive health promotion program. A prediction model was developed by the use of a Random forests and the results of the developed model were compared with those of a decision tree analysis based on classification and regression tree (CART).

Results: The random forests model predicted education level, subjective health, subjective friendship, subjective family bond, mean monthly family income, age, smoking, living with a spouse or not, depression history, drinking, and regular exercise as the major variables. The analysis results of test data showed that the accuracy of the random forests was 72.3% and that of the CART model was 70.9%.

Conclusion: It is necessary to develop a customized health promotion program considering the characteristics of subjects in order to implement a program effectively based on the developed model to predict participation in a cognitive health promotion program.




1. Lee MS (2009). Structures of health inequali-ties of Korean elderly: Analysis of Kore-an Longitudinal Study of Ageing. HASS, 25(1): 5-32.
2. Turner FJ (2000). Mental health and the elderly. Manhattan: Simon and Schuster. New York.
3. Byeon H, Lee Y, Lee SY, et al (2015). Asso-ciation of alcohol drinking with verbal and visuospatial memory impairment in older adults: Clinical Research Center for Dementia of South Korea (CREDOS) study. Int Psychogeriatr, 27(3): 455-61.
4. Blondell SJ, Hammersley-Mather R, Veer-man JL (2014). Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies. BMC public health, 14: 510.
5. Lee Y (2000). The predictive value of self as-sessed general, physical, and mental health on functional decline and mortality in older adults. J Epidemiol Community Health, 54(2): 123-129.
6. Cho MJ (2009). The Prevalence and risk fac-tors of dementia in the Korean elderly. Health and Welfare Policy Forum, 156: 43-8.
7. Yeunhee Kwak, Chung H, Yoonjung Kim (2017). Differences in Health-related Quality of Life and Mental Health by Liv-ing Arrangement among Korean Elderly in the KNHANES 2010–2012. Iran J Pub-lic Health, 46(11): 1512-1520.
8. Kiraly SJ (2011). Mental health promotion for seniors. BCMJ, 53(7): 336-40.
9. Prince M, Patel V, Saxena S, et al (2007). No health without mental health. The Lancet, 370(9590): 859-877.
10. Haigh EA, Bogucki OE, Sigmon ST, Blazer DG (2018). Depression among older adults: a 20-year update on five common myths and misconceptions. Am J Geriatr Psychiatry, 26(1): 107-122.
11. Potter GG, Steffens DC (2007). Contribu-tion of depression to cognitive impair-ment and dementia in older adults. Neu-rologist, 13(3): 105-17.
12. Strawbridge WJ, Shema SJ, Cohen RD, Kaplan GA (2001). Religious attendance increases survival by improving and maintaining good health behaviors, men-tal health, and social relationships. Ann Behav Med, 23(1): 68-74.
13. Bartels SJ, Coakley EH, Zubritsky C, et al (2004). Improving access to geriatric mental health services: a randomized trial comparing treatment engagement with integrated versus enhanced referral care for depression, anxiety, and at risk alco-hol use. Am J Psychiatry, 161(8): 1455-62.
14. Song MS, Boo S (2016). Factors affecting the intention to participate in healthcare programs among elders living alone. J Korean Acad Community Health Nurs, 27(4): 319-326.
15. Yoon HS, Lee H, Lee SK (2008). Factors as-sociated with the use of health promo-tion program-Seoul community health center. Health and Social Welfare Review, 28(2): 157-84.
16. Van der Roest HG, Meiland FJ, Comijs HC, et al (2009). What do community-dwelling people with dementia need? a survey of those who are known to care and welfare services. Int Psychogeriatr, 21(5): 949-65.
17. Beattie A, Daker‐White G, Gilliard J, Means R (2004). ‘How can they tell?’ a qualitative study of the views of younger people about their dementia and dementia care services. Health Soc Care Community, 12(4): 359-68.
18. Drewnowski A, Monsen E, Birkett D, et al (2003). Health screening and health pro-motion programs for the elderly. Dis Manag Health Out, 11(5): 299-309.
19. Byeon H (2015). A prediction model for mild cognitive impairment using random forests. IJACSA, 6(12): 8-12.
20. Seoul Welfare Foundation (2016). Seoul Wel-fare Panel Study 2015. Seoul: Seoul Welfare Foundation. Seoul.
21. Herrmann N, Mittmann N, Silver IL, et al (1996). A validation study of the Geriatric Depression Scale short form. Int J Geriatr Psychiatry, 11(5): 457-60.
22. Maroufizadeh S, Amini P, Hosseini M, et al (2018). Determinants of Cesarean Section among Primiparas: A Comparison of Classification Methods. Iran J Public Health, 47(12): 1913-1922.
23. Hamidi O, Poorolajal J, Farhadian M, Tapak L (2016). Identifying important risk fac-tors for survival in kidney graft failure pa-tients using random survival forests. Iran J Public Health, 45(1): 27-33.
24. Byeon H, Jin H, Cho S (2017). Development of Parkinson's disease dementia predic-tion model based on verbal memory, visuospatial memory, and executive func-tion. Journal of Medical Imaging and Health In-formatics, 7(7): 1517-1521.
25. Brieman L, Friedman J, Olshen RA, Stone CJ (1984). Classification and Regression Trees. Chapman & Hall. New York.
26. Daraei A, Hamidi H (2017). An efficient predictive model for myocardial infarc-tion using cost-sensitive J48 model. Iran J Public Health, 46(5): 682-692.
27. Amini P, Ahmadinia H, Poorolajal J, Amiri MM (2016). Evaluating the high risk groups for suicide: A comparison of lo-gistic regression, support vector machine, decision tree and artificial neural network. Iran J Public Health, 45(9): 1179-1187.
28. Byeon, H (2015). The risk factors of laryn-geal pathology in Korean adults using a decision tree model. J Voice, 29(1): 59-64.
29. Cho S, Kim Y, Lim H, Park Y, Lee W (2006). Internet users' intention to partic-ipate in preventive program of depres-sion. KHEP, 24(2): 1-16.
30. Valenzuela MJ, Sachdev P (2006). Brain re-serve and dementia: a systematic review. Psychol Med, 36(4): 441-54.
31. Meng X, D’Arcy C (2012). Education and dementia in the context of the cognitive reserve hypothesis: a systematic review with meta-analyses and qualitative anal-yses. PloS one, 7(6): e38268.
32. Stern Y, Habeck C, Moeller J, et al (2005). Brain networks associated with cognitive reserve in healthy young and old adults. Cereb Cortex, 15(4): 394-402.
33. Fotuhi M, Hachinski V, Whitehouse PJ (2009). Changing perspectives regarding late-life dementia. Nat Rev Neurol, 5(12): 649-58.
IssueVol 50 No 2 (2021) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v50i2.5346
Cognitive health promotion program Prediction model Decision tree Random forest

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
BYEON H. Can the Random Forests Model Improve the Power to Predict the Inten-tion of the Elderly in a Community to Participate in a Cognitive Health Promotion Program?. Iran J Public Health. 2021;50(2):315-324.