Myonghwa Park; Sora Choi; A Mi Shin; Chul Hoi Koo

doi:10.4040/jkan.2013.43.1.1

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Original Article
Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis: Myonghwa Park, Sora Choi, A Mi Shin, Chul Hoi Koo; Journal of Korean Academy of Nursing 2013;43(1):1-10.
DOI: https://doi.org/10.4040/jkan.2013.43.1.1
Published online: February 28, 2013

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: May 25, 2012 • Accepted: August 31, 2012

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Abstract
NOTES
REFERENCES

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

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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Figure 1

Process of data mining.

Figure 2

Input variables.

Figure 3

Decision tree of C&RT model.

Table 1

Predictive Performance according to Modeling Methods

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)

Figure & Data

REFERENCES

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

J Korean Acad Nurs. 2013;43(1):1-10. Published online February 28, 2013

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

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Figure

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

Figure 1 Process of data mining.

Figure 2 Input variables.

Figure 3 Decision tree of C&RT model.

Figure 1

Figure 2

Figure 3

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

Table 1

Predictive Performance according to Modeling Methods

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)

Table 1 Predictive Performance according to Modeling Methods

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)