Open Access, Peer-reviewed
eISSN 2093-758X
    J Korean Acad Nurs. 2013 Feb;43(1):1-10. Korean.
    Published online Feb 28, 2013.
    https://doi.org/10.4040/jkan.2013.43.1.1
    © 2013 Korean Society of Nursing Science
    Original Article

    Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis

    Myonghwa Park,1 Sora Choi,1 A Mi Shin,2 and Chul Hoi Koo3
      • 1College of Nursing, Chungnam National University, Daejeon, Korea.
      • 2Kyungpook National University Medical Center, Chilgok-gun, Gyeongbuk, Korea.
      • 3Department of Public Administration, Cheongju University, Chungbuk, Korea.
    • Address reprint requests to: Park, Myonghwa. College of Nursing, Chungnam National University, 55 Munhwa-ro, Jung-gu, Daejeon 301-747, Korea. Tel: +82-42-580-8328, Fax: +82-42-580-8309, Email: mhpark@cnu.ac.kr
    Received May 25, 2012; Accepted August 31, 2012.

    Abstract

    Purpose

    The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method.

    Methods

    A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs.

    Results

    The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease.

    Conclusion

    The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

    Keywords
    Data mining; Decision trees; Depression; Aged

    Figures

    Figure 1
    Process of data mining.

    Figure 2
    Input variables.

    Figure 3
    Decision tree of C&RT model.

    Tables

    Table 1
    Predictive Performance according to Modeling Methods

    Table 2
    General Characteristics of Participants (N=14,970)

    Notes

    This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0024922).

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    MeSH Terms
    Activities of Daily Living
    Aged
    Aged, 80 and over
    Chronic Disease
    Data Mining
    Decision Trees
    Depression/psychology*
    Female
    Health Behavior
    Health Status
    Humans
    Leisure Activities
    Male
    Middle Aged
    Quality of Life
    Socioeconomic Factors
    Metrics
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