Application of artificial neural network analysis and decision tree analysis to develop a model for predicting life satisfaction of the elderly in south korea

  • Authors

    • Haewon Byeon
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.12.11116
  • Datamining, Neural Network, Decision Tree, Risk Factors, Life Satisfaction

  • Background/Objectives: This study developed a prediction model with taking into account various factors that could affect the life satisfaction of the elderly in South Korea by using data mining techniques.

    Methods/Statistical analysis: This study analyzed the data of 2,111 elderly (879 males and 1, 232 females) who were equal to or older than 60 among 7,761 people completed the Seoul Welfare Pane Study 2010. The life satisfaction, a result variable, was classified as ‘satisfactory’, ‘normal’, and ‘dissatisfactory’ based on the question of ‘how are you satisfied with your current life?’The latent factors of the life satisfaction of the elderly were explored by using the neural network. The decision tree model was constructed by using the classification and regression tree (CART) algorithm.

    Findings: Subjective friendship, subjective health status, subjective family relationship, and the highest level of education were significant classification variables. The most predominant predictive variable was subjective friendship. Moreover, it was predicted that ‘the elderly with good subjective friendship and subjective health’ and ‘the elderly with good subjective friendship, subjective health, and family relationship and whose highest level of education was higher than middle school graduate’ would be groups with high life satisfaction.

    Improvements/Applications: It is necessary to expand the perceived social network support for promoting the family relationship and friendship as well as the health enhancement in order to improve the life satisfaction of the elderly

  • References

    1. [1] National Statistical Office, Estimates and Projections of the Population of the Korea, National Statistical Office, Seoul, 2016.

      [2] Statistics Korea, Statistics on the Elderly, National Statistical Office, Seoul, 2013.

      [3] He W, Goodkind D, Kowal P. R, An aging world: 2015, United States Census Bureau, Washington, DC, 2016.

      [4] Chung K. H, Lee Y. K, Park B. M, Lee S. J, Lee Y, Analysis of the Survey of Living Conditions and Welfare Needs of Korean Older Persons 2011, Korea Institute for Health and Social Affairs, Seoul, 2012.

      [5] Jung K. H, Son C. K, Park B. M, Policy Challenges posed by Emerging ‘New Class of Older Persons’, Korea Institute for Health and Social Affairs, Seoul, 2010.

      [6] Schiffman L. G, Sherman E, Value orientations of new-age elderly: the coming of an ageless market. Journal of Business Research, 1991, 22(2), pp. 187-94.

      [7] Corrigan J. D, Kolakowsky-Hayner S, Wright J, Bellon K, Carufel P, The satisfaction with life scale. The Journal of Head Trauma Rehabilitation, 2013, 28(6), pp. 489-91.

      [8] Durand M, The OECD Better Life Initiative: How's Life? and the Measurement of Wellâ€Being. Review of Income and Wealth, 2015, 61(1), pp. 4-17.

      [9] Banting K, Sharpe A, France, St-Hilaire, The Review of Performance and Social Progress. The Institute for Research on Public Policy, Montreal, 2001.

      [10] Blanchflower D. G, Oswald A. J, Well-being over time in Britain and the USA. Journal of Public Economics, 2004, 88(7), pp. 1359-86.

      [11] Kirchengast S, Haslinger B, Gender differences in health-related quality of life among healthy aged and old-aged Austrians: cross-sectional analysis. Gender Medicine, 2008, 5(3), pp. 270-78.

      [12] Breeze E, Jones D. A, Wilkinson P, Bulpitt C. J, Grundy C, Latif A. M, Fletcher A. E, Area deprivation, social class, and quality of life among people aged 75 years and over in Britain. International Journal of Epidemiology, 2005, 34(2), pp. 276-83.

      [13] Sun F, Norman I. J, While A. E, Physical activity in older people: a systematic review. BMC Public Health, 2013, 13(1), pp. 449.

      [14] Enkvist Å, Ekström H, Elmståhl S, What factors affect life satisfaction (LS) among the oldest-old?. Archives of Gerontology and Geriatrics, 2012, 54(1), 140-45.

      [15] Depp C. A, Jeste D. V, Definitions and predictors of successful aging: a comprehensive review of larger quantitative studies. The American Journal of Geriatric Psychiatry, 2006, 14(1), pp. 6-20.

      [16] Seoul Welfare Foundation, Seoul Welfare Panel Study 2010, Seoul Welfare Foundation, Seoul, 2010.

      [17] Rafiq M. Y, Bugmann G, Easterbrook D. J, Neural network design for engineering applications. Computers & Structures, 2001, 79(17), pp. 1541-52.

      [18] Byeon H, Cho S, The Factors of Subjective Voice Disorder Using Integrated Method of Decision Tree and Multi-Layer Perceptron Artificial Neural Network Algorithm. International Journal of Advanced Computer Science and Applications, 2016, 7(5), pp. 112-16.

      [19] Cho S, Yu S, Byeon H, A Prediction Model for Benign Laryngeal Disease Using Supervised Learning Techniques. International Journal of Bio-Science and Bio-Technology, 2016, 8(4), pp. 105-10.

      [20] Byeon H, The risk factors of laryngeal pathology in Korean adults using a decision tree model. Journal of Voice, 2015, 29(1), pp. 59-64.

      [21] Lee R, Byeon H, The factors that affects the needs of the Korean language education in children in multi-cultural families: results of a national cross sectional survey. International Journal of Applied Engineering Research, 2015, 10(10), pp. 26657-67.

      [22] Lee J, Predictors of life satisfaction among older adults in s. Korea: differences by education level. Journal of the Korea Gerontological Society, 2010, 30(3), pp. 709-26.

      [23] Hu S, Kim J, Analysis of multi-level effectiveness on life satisfaction in old age at KLIPS 2006. Journal of the Korea Gerontological Society, 2011, 31(2), pp. 407-18.

      [24] Back J. U, Kim S. W, Kim M. Y, Factors that influence satisfaction level towards the life by the leisure activity of the users of the senior citizens’ leisure welfare facilities. Korean Journal of Clinical Social Work, 2010, 7(1), pp. 37-58.

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  • How to Cite

    Byeon, H. (2018). Application of artificial neural network analysis and decision tree analysis to develop a model for predicting life satisfaction of the elderly in south korea. International Journal of Engineering & Technology, 7(2.12), 161-166. https://doi.org/10.14419/ijet.v7i2.12.11116