Developing a model to predict the Social Activity Participation of the senior citizens living in South Korea by Combining Artificial Neural Network and QUEST Algorithm

  • Authors

    • Haewon Byeon
    2019-01-02
    https://doi.org/10.14419/ijet.v8i1.4.25229
  • social activities, quality of life, subjective health status, neural network, decision tree model

  • Social participation in old age is important in terms of the quality of life. The objectives of this study were to establish a statistical classification model, which can predict the social participation in the old age and provide baseline data for achieving successful aging by using a dataset representing South Korea and combining artificial neural network with QUEST algorithm. This study analyzed the data of 1,864 subjects (829 males and 1,035 females) who were equal to or older than 65 years and completed the 2015 Community Health Survey. The dependent variable was the social activities within the past one month (yes/no). The factors associated with the social participation in the old age were analyzed by using the neural network and QUEST algorithm. Among 1,864 subjects, 1,035 senior citizens (55.5%) did not have social activities during the past one month. The results of established QUEST algorithm classification model revealed that subjective health, age, the frequency of meeting a neighbor, the frequency of meeting a relative, the highest level of education, and walking per week were significant classification variables. Among them, subjective health status was a predictive factor associated with it first. The results of the developed model revealed that social care and institutional measures are needed to promote the social participation of the elderly for achieving the successful aging of the aging society.

     

     

  • References

    1. [1] National Statistical Office, Statistics of the aged, Daejeon : National Statistical Office, (2017), pp.13-81.

      [2] Mollenkopf H & Walker A, Quality of life in old age, Dordrecht the Netherlands: Springer, (2007), pp.45-102.

      [3] Sarvimäki A & Stenbockâ€Hult B, “Quality of life in old age described as a sense of wellâ€being, meaning and valueâ€, Journal of advanced nursing, Vol.32, No.4, (2000), pp.1025-1033.

      [4]. Kwon S & Reich MR, “The changing process and politics of health policy in Korea. Journal of Health Politicsâ€, Policy and Law, Vol.30, No.6, (2005), pp.1003-1026.

      [5] Baltes PB & Smith J, “New frontiers in the future of aging: From successful aging of the young old to the dilemmas of the fourth ageâ€, Gerontology, Vol.49, No.2, (2003), pp.123-135.

      [6] Rowe JW & Kahn RL, “The future of agingâ€, Contemporary longterm care, Vol.22, No.2, (1999), pp.36-38.

      [7] Menec VH, “The relation between everyday activities and successful aging: A 6-year longitudinal studyâ€, The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, Vol.58, No.2, (2003), pp.S74-S82.

      [8] Fox KR, “The influence of physical activity on mental well-beingâ€, Public health nutrition, Vol.2, No.3a, (1999), pp.411-418.

      [9] Lee TW, Ko IS & Lee KJ, “Health promotion behaviors and quality of life among community-dwelling elderly in Korea: A cross-sectional surveyâ€, International journal of nursing studies, Vol.43, No.3, (2006), pp.293-300.

      [10] Tajvar M, Arab M & Montazeri A, “Determinants of health-related quality of life in elderly in Tehranâ€, Iran. BMC public health, Vol.8, No.1, (2008), pp.323.

      [11] Bowling A & Dieppe P, “What is successful ageing and who should define it?â€, Bmj, Vol.331, No.7531, (2005), pp.1548-1551.

      [12] Lennartsson C & Silverstein M, “Does engagement with life enhance survival of elderly people in Sweden? The role of social and leisure activitiesâ€, The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, Vol.56, No.6, (2001), pp.S335-S342.

      [13] Byeon H & Cho S, “The Factors of Subjective Voice Disorder Using Integrated Method of Decision Tree and Multi-Layer Perceptron Artificial Neural Network Algorithmâ€, International Journal Of Advanced Computer Science and Applications, Vol.7, No.5, (2016), pp.112-116.

      [14] Byeon H, “The Factors of Participating in a Smoking Cessation Program using Integrated Method of Decision Tree and Neural Network Algorithmâ€, Journal of the Korea Convergence Society, Vol.6, No.2, (2015), pp.25-30.

      [15] Kim YJ, Ko YS, Seo ER, Park JR, Lee JG, Cho H, Kim YT, Kim HJ, Kim SY, Lim DS, Kang YH, Lee TY, Kim JS, Kim YA, Choi BY & Lee DH, “Overview of Korean community health surveyâ€, Journal of the Korean Medical Association, Vol.55, No.1, (2012), pp.74-83.

      [16] Kaynar O, Yilmaz I & Demirkoparan F, “Forecasting of natural gas consumption with neural network and neuro fuzzy systemâ€, Energy Education Science and Technology Part A: Energy Science and Research, Vol.26, No,2, (2011), pp.221-238.

      [17] Byeon, H, “A prediction model for peer Attachment in Korean Female Adolescents using Back Propagation Neural Networkâ€, Journal of Engineering and Applied Sciences, Vol.7, No.2.33, (2018), pp.27-30.

      [18] Byeon H, “Application of Artificial Neural Network Analysis and Decision Tree Analysis to Develop a Model for Predicting Life Satisfaction of the Elderly in South Koreaâ€, Journal of Engineering and Applied Sciences, Vol.7, No.2.12, (2018), pp.161-166.

      [19] Takahashi NS, de Souza A & Tosin M, “Execution time of QUEST algorithm in embedded processorsâ€, In Proceedings of 2009 Brazilian Symposium on Aerospace Engineering & Applications/3rd CTA-DLR Workshop on Data Analysis & Flight Control, (2009), pp. 14-16.

      [20] Cheng Y & Shuster MD, “Improvement to the Implementation of the QUEST Algorithm. Journal of Guidanceâ€, Control, and Dynamics, Vol.37, No.1, (2013), pp.301-305.

      [21] Cho S & Byeon H, “Predictive modeling of hypertension in Korean old adults using QUEST algorithmâ€, Asia Life Sciences, Vol.12, (2015), pp.21-30.

      [22] Takeyachi Y, Konno SI, Otani K, Yamauchi K, Takahashi I, Suzukamo Y & Kikuchi SI, “Correlation of low back pain with functional status, general health perception, social participation, subjective happiness, and patient satisfactionâ€, Spine, Vol.28, No.13, (2003), pp.1461-1466.

      [23] Pinquart M, “Correlates of subjective health in older adults: A meta-analysisâ€, Psychology and aging, Vol.16, No.3, (2001), pp.414-426.

      [24] Seeman TE, Lusignolo TM, Albert M & Berkman L, “Social relationships, social support, and patterns of cognitive aging in healthy, high-functioning older adults: MacArthur studies of successful agingâ€. Health psychology, Vol.20, No.4, (2001), pp.243.

      [25] Menec VH, “The relation between everyday activities and successful aging: A 6-year longitudinal studyâ€, The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, Vol.58, No.2, (2003), pp.S74-S82.

      [26] Kwon S, “Future of long-term care financing for the elderly in Koreaâ€, Journal of aging & social policy, Vol.20, No.1, (2008), pp.119-136.

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    Byeon, H. (2019). Developing a model to predict the Social Activity Participation of the senior citizens living in South Korea by Combining Artificial Neural Network and QUEST Algorithm. International Journal of Engineering & Technology, 8(1.4), 214-221. https://doi.org/10.14419/ijet.v8i1.4.25229