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Park, Choi, Shin, and Koo: Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis
Original Article
Journal of Korean Academy of Nursing

Journal of Korean Academy of Nursing 2013; 43(1): 1-10.

Published online: 28 February 2013

DOI: https://doi.org/10.4040/jkan.2013.43.1.1

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

Myonghwa Park1, Sora Choi1, A Mi Shin2, 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, mhpark@cnu.ac.kr

Received 25 May 2012       Accepted 31 August 2012

© 2013 Korean Society of Nursing Science

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 and Tables

Figure 1
Process of data mining.
jkan-43-1-g001
Figure 2
Input variables.
jkan-43-1-g002
Figure 3
Decision tree of C&RT model.
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Table 1
Predictive Performance according to Modeling Methods
jkan-43-1-i001

C&RT=Classification & regression tree; QUEST=Quick, unbiased, efficient, statistical tree; CHAID=CHi-squared automatic interaction detection.

Table 2
General Characteristics of Participants (N=14,970)
jkan-43-1-i002

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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