Journal of Korean Academy of Nursing

A Mi Shin

2 Articles

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; 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

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.

Citations

Citations to this article as recorded by

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta
Qingqing Tian, Hang Gao, Yu Tian, Qiongyao Wang, Lei Guo, Qihui Chai
Frontiers in Marine Science.2024;[Epub] CrossRef
A prediction model for adolescents’ skipping breakfast using the CART algorithm for decision trees: 7th (2016–2018) Korea National Health and Nutrition Examination Survey
Sun A Choi, Sung Suk Chung, Jeong Ok Rho
Journal of Nutrition and Health.2023; 56(3): 300. CrossRef
Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest
Kyu-Min Kim, Jae-Hak Kim, Hyun-Sill Rhee, Bo-Young Youn
Scientific Reports.2023;[Epub] CrossRef
A Predictive Model of Ischemic Heart Disease in Middle-Aged and Older Women Using Data Mining Technique
Jihye Lim
Journal of Personalized Medicine.2023; 13(4): 663. CrossRef
A Comparative Study of Predictive Factors for Passing the National Physical Therapy Examination using Logistic Regression Analysis and Decision Tree Analysis
So Hyun Kim, Sung Hyoun Cho
Physical Therapy Rehabilitation Science.2022; 11(3): 285. CrossRef
Occupational accident prediction modeling and analysis using SHAP
Hyung-Rok Oh, Ae-Lin Son, ZoonKy Lee
Journal of Digital Contents Society.2021; 22(7): 1115. CrossRef
FACTORS DETERMINING THE EXTENT OF GDPR IMPLEMENTATION WITHIN ORGANIZATIONS: EMPIRICAL EVIDENCE FROM CZECH REPUBLIC
Adam Faifr, Martin Januška
Journal of Business Economics and Management.2021; 22(5): 1124. CrossRef
Evaluation of Food Labeling Policy in Korea: Analyzing the Community Health Survey 2014–2017
Heui Sug Jo, Su Mi Jung
Journal of Korean Medical Science.2019;[Epub] CrossRef
Factors Influencing Depression in Middle Aged Women: Focused on Quality of life on Menopause
Jung Nam Sohn
Journal of Health Informatics and Statistics.2018; 43(2): 148. CrossRef
Song-Induced Autobiographical Memory of Patients With Early Alzheimer's Dementia
Seung Ah Han
Journal of Music and Human Behavior.2016; 13(2): 49. CrossRef
Factors Affecting on Life Satisfaction of Elderly after Total Knee Arthroplasty
You-Jin Park, Eun-Hee Park
Journal of Digital Convergence.2016; 14(9): 563. CrossRef
Application of big data analysis with decision tree for the foot disorder
Jung-Kyu Choi, Keun-Hwan Jeon, Yonggwan Won, Jung-Ja Kim
Cluster Computing.2015; 18(4): 1399. CrossRef
A Study on Comparison of Classification and Regression Tree and Multiple Regression for Predicting of Soldiers' Depression
Chung Hee Woo, Ju Young Park
Journal of Korean Academy of Psychiatric and Mental Health Nursing.2014; 23(4): 268. CrossRef
Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly
Myonghwa Park, Hyeyoung Kim, Sun Kyung Kim
Healthcare Informatics Research.2014; 20(1): 30. CrossRef
The predictability of dentoskeletal factors for soft-tissue chin strain during lip closure
Yun-Hee Yu, Yae-Jin Kim, Dong-Yul Lee, Yong-Kyu Lim
The Korean Journal of Orthodontics.2013; 43(6): 279. CrossRef
Some fixed point theorems in locally p-convex spaces
Leila Gholizadeh, Erdal Karapınar, Mehdi Roohi
Fixed Point Theory and Applications.2013;[Epub] CrossRef
Factors Influencing Depressive Symptoms in Community Dwelling Older People
Jung Nam Sohn
Journal of Korean Academy of Psychiatric and Mental Health Nursing.2013; 22(2): 107. CrossRef

236 View
0 Download
17 Crossref

The Effects of Hope Intervention on Hope and Depression of Cancer Patients Staying at Home: A Mi Shin, Jeong Sook Park; Journal of Korean Academy of Nursing 2007;37(6):994-1002. Published online March 28, 2017; DOI: https://doi.org/10.4040/jkan.2007.37.6.994

Abstract PDF

PURPOSE: This study was to identify the effects of hope intervention on hope and depression of cancer patients staying at home. METHODS: The study design was a randomized control group design. The subjects consisted of forty cancer patients randomly selected who were registered at S-Gu Public Health Center. Hope intervention, which was composed of hope assessment, hope objective setting, positive self identity formation, therapeutic relationships, spiritual & transcendental process improvement, positive environmental formation and hope evaluation, was provided from November 20, 2006 to January 26, 2007. RESULTS: The 1-1 hypothesis, "The experimental group which received hope intervention will have a higher score of hope than the control group", was supported(t=-3.253, p= .003). The 1-2 hypothesis, "The experimental group which received hope intervention will have a higher level of hope index than the control group", was supported (t=-4.001, p= .000). Therefore the 1st hypothesis, "The experimental group which received hope intervention will have a higher level of hope than the control group" was supported. The 2nd hypothesis, "The experimental group which received hope intervention will have a lower level of depression than the control group", was not supported (t=1.872, p= .070). CONCLUSION: Hope intervention is an effective nursing intervention to enhance hope for patient with cancer.

Citations

Citations to this article as recorded by

Culturalizing theory and research on cognitive models of hope
Allan B. I. Bernardo, Sixtus Dane A. Ramos
Frontiers in Psychology.2024;[Epub] CrossRef
EFFECT OF GROUP-BASED HOPE INTERVENTION ON DEPRESSION IN FEMALE INMATES
Mei Rianita Elfrida Sinaga, Megah Andriany, Artika Nurrahima
Belitung Nursing Journal.2020; 6(4): 116. CrossRef
Effects of a Group Coaching Program on Depression, Anxiety and Hope in Women with Breast Cancer Undergoing Chemotherapy
So Ryoung Seong, Moon-kyung Cho, Jeeyoon Kim, Yeo Ok Kim
Asian Oncology Nursing.2017; 17(3): 188. CrossRef
Effects of Horticultural Therapy Program on State-Anxiety, Fatigue and Quality of Life among Women Cancer Survivors
Kyong Ok Oh, Moon Hee Gang, Kwon Sook Jung
Asian Oncology Nursing.2012; 12(2): 125. CrossRef

115 View
4 Download
4 Crossref

Author index