Journal of Korean Academy of Nursing

Research Paper

Comparison of the Prediction Model of Adolescents’ Suicide Attempt Using Logistic Regression and Decision Tree: Secondary Data Analysis of the 2019 Youth Health Risk Behavior Web-Based Survey: Lee, Yoonju , Kim, Heejin , Lee, Yesul , Jeong, Hyesun; J Korean Acad Nurs 2021;51(1):40-53. Published online February 28, 2021; DOI: https://doi.org/10.4040/jkan.20207

Purpose
The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0.
Results
A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model.
Conclusion
Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.

Citations

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Development of Prediction Model for Suicide Attempts Using the Korean Youth Health Behavior Web-Based Survey in Korean Middle and High School Students
Younggeun Kim, Sung-Il Woo, Sang Woo Hahn, Yeon Jung Lee, Minjae Kim, Hyeonseo Jin, Jiyeon Kim, Jaeuk Hwang
Journal of Korean Neuropsychiatric Association.2023; 62(3): 95. CrossRef
Effects of Stress on Suicide Behavior among Adolescents: An Analysis of Online Survey Data on Youth Health Behavior Using Propensity Score Matching
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Korean Journal of Stress Research.2021; 29(3): 199. CrossRef

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Original Articles

Analysis of Subgroups with Lower Level of Patient Safety Perceptions Using Decision-Tree Analysis: Shin, Sun Hwa; J Korean Acad Nurs 2020;50(5):686-698. Published online October 31, 2020; DOI: https://doi.org/10.4040/jkan.20044

Abstract PDF

Purpose
This study was aimed to investigate experiences, perceptions, and educational needs related to patient safety and the factors affecting these perceptions.
Methods
Study design was a descriptive survey conducted in November 2019. A sample of 1,187 Koreans aged 20-80 years participated in the online survey. Based on previous research, the questionnaire used patient safety-related and educational requirement items, and the Patient Safety Perception Scale. Descriptive statistics and a decision tree analysis were performed using SPSS 25.0.
Results
The average patient safety perception was 71.71 (± 9.21). Approximately 95.9% of the participants reported a need for patient safety education, and 88.0% answered that they would participate in such education. The most influential factors in the group with low patient safety perceptions were the recognition of patient safety activities, age, preference of accredited hospitals, experience of patient safety problems, and willingness to participate in patient safety education.
Conclusion
It was confirmed that the vulnerable group for patient safety perception is not aware of patient safety activities and did not prefer an accredited hospital. To prevent patient safety accidents and establish a culture of patient safety, appropriate educational strategies must be provided to the general public.

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Structural Topic Modeling Analysis of Patient Safety Interest among Health Consumers in Social Media
Nari Kim, Nam-Ju Lee
Journal of Korean Academy of Nursing.2024; 54(2): 266. CrossRef
Analysis of Factors Related to Domestic Patient Safety Incidents Using Decision Tree Technique
Jieun Shin, Ji-Hoon Lee, Nam-Yi Kim
Risk Management and Healthcare Policy.2023; Volume 16: 1467. CrossRef
Smoking Awareness and Intention to Quit Smoking in Smoking Female Workers: Secondary Data Analysis
Eun-Hye Lee, Sun-Hwa Shin, Goo-Churl Jeong
International Journal of Environmental Research and Public Health.2022; 19(5): 2841. CrossRef
Development and Effectiveness of a Patient Safety Education Program for Inpatients
Sun Hwa Shin, Mi Jung Kim, Ho Jin Moon, Eun Hye Lee
International Journal of Environmental Research and Public Health.2021; 18(6): 3262. CrossRef

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Prediction Models of Mild Cognitive Impairment Using the Korea Longitudinal Study of Ageing: Park, Hyojin , Ha, Juyoung; J Korean Acad Nurs 2020;50(2):191-199. Published online April 30, 2020; DOI: https://doi.org/10.4040/jkan.2020.50.2.191

Abstract PDF

Purpose
The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI).
Methods
This study was a secondary data analysis research using data from “the 4th Korea Longitudinal Study of Ageing” of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a c2-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2.
Results
In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors.
Conclusion
The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.

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Journal of Korean Academy of Nursing.2025; 55(1): 50. CrossRef
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Identification of Subgroups with Lower Level of Stroke Knowledge Using Decision-tree Analysis: Hyun Kyung Kim, Seok Hee Jeong, Hyun Cheol Kang; J Korean Acad Nurs 2014;44(1):97-107. Published online February 28, 2014; DOI: https://doi.org/10.4040/jkan.2014.44.1.97

Abstract PDF

Purpose

This study was performed to explore levels of stroke knowledge and identify subgroups with lower levels of stroke knowledge among adults in Korea.

Methods

A cross-sectional survey was used and data were collected in 2012. A national sample of 990 Koreans aged 20 to 74 years participated in this study. Knowledge of risk factors, warning signs, and first action for stroke were surveyed using face-to-face interviews. Descriptive statistics and decision tree analysis were performed using SPSS WIN 20.0 and Answer Tree 3.1.

Results

Mean score for stroke risk factor knowledge was 7.7 out of 10. The least recognized risk factor was diabetes and four subgroups with lower levels of knowledge were identified. Score for knowledge of stroke warning signs was 3.6 out of 6. The least recognized warning sign was sudden severe headache and six subgroups with lower levels of knowledge were identified. The first action for stroke was recognized by 65.7 percent of participants and four subgroups with lower levels of knowledge were identified.

Conclusion

Multi-faceted education should be designed to improve stroke knowledge among Korean adults, particularly focusing on subgroups with lower levels of knowledge and less recognition of items in this study.

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A Predictive Model of Depression in Rural Elders-Decision Tree Analysis: Seong Eun Kim, Sun Ah Kim; J Korean Acad Nurs 2013;43(3):442-451. Published online June 28, 2013; DOI: https://doi.org/10.4040/jkan.2013.43.3.442

Abstract PDF

Purpose

This descriptive study was done to develop a predictive model of depression in rural elders that will guide prevention and reduction of depression in elders.

Methods

A cross-sectional descriptive survey was done using face-to-face private interviews. Participants included in the final analysis were 461 elders (aged≥ 65 years). The questions were on depression, personal and environmental factors, body functions and structures, activity and participation. Decision tree analysis using the SPSS Modeler 14.1 program was applied to build an optimum and significant predictive model to predict depression in rural elders.

Results

From the data analysis, the predictive model for factors related to depression in rural elders presented with 4 path-ways. Predictive factors included exercise capacity, self-esteem, farming, social activity, cognitive function, and gender. The accuracy of the model was 83.7%, error rate 16.3%, sensitivity 63.3%, and specificity 93.6%.

Conclusion

The results of this study can be used as a theoretical basis for developing a systematic knowledge system for nursing and for developing a protocol that prevents depression in elders living in rural areas, thereby contributing to advanced depression prevention for elders.

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Frontiers in Public Health.2022;[Epub] CrossRef
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Ji-Ae Son, Soon-Rim Suh, Mihan Kim
Journal of Korean Gerontological Nursing.2015; 17(1): 56. CrossRef
The Factors Associated with Depression in the Elderly Male: Based on the 5th Korea National Health and Nutrition Examination Survey
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Korean Journal of Adult Nursing.2015; 27(5): 583. CrossRef
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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

Abstract PDF

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.

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A Prediction Model for Internet Game Addiction in Adolescents: Using a Decision Tree Analysis: Ki Sook Kim, Kyung Hee Kim; J Korean Acad Nurs 2010;40(3):378-388. Published online June 30, 2010; DOI: https://doi.org/10.4040/jkan.2010.40.3.378

Abstract PDF

Purpose

This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors.

Methods

The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors.

Results

From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet cafe@, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents).

Conclusion

The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

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