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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
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Abstract
PDF - 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
Citations to this article as recorded by
- Public discourse on substance use behavior as a driver of public policy: a scoping review of South Korean academic and official literature
Meekang Sung, Jihye Han, Carrie G. Wade, Vaughan W. Rees
Addiction Research & Theory.2025; : 1. CrossRef - Factors Influencing Suicidal Ideation in Female Adolescents With Smartphone Overdependence
Hyeongyeong Yoon
Journal of Pediatric Health Care.2024;[Epub] CrossRef - The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review
Nibene H. Somé, Pardis Noormohammadpour, Shannon Lange
Frontiers in Psychiatry.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 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
Chung Hee Woo, Ju Young Park
Korean Journal of Stress Research.2021; 29(3): 199. CrossRef
- Public discourse on substance use behavior as a driver of public policy: a scoping review of South Korean academic and official literature
- 229 View
- 16 Download
- 3 Web of Science
- 6 Crossref
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
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Abstract
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- 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. -
Citations
Citations to this article as recorded by- 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
- Structural Topic Modeling Analysis of Patient Safety Interest among Health Consumers in Social Media
- 145 View
- 4 Download
- 5 Web of Science
- 4 Crossref
- 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
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Abstract
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- 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. -
Citations
Citations to this article as recorded by- Cognitive Dysfunction Prediction Model with Lifelog Dataset based on Random Forest and SHAP
Myeongjin Lee, Jongun Lee, Hanjun Lee
The Journal of Korean Institute of Information Technology.2024; 22(1): 1. CrossRef - Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study
Shuyi Jin, Chenxi Li, Jiani Miao, Jingyi Sun, Zhenqing Yang, Xingqi Cao, Kaili Sun, Xiaoting Liu, Lina Ma, Xin Xu, Zuyun Liu
Journal of the American Medical Directors Association.2023; 24(12): 1959. CrossRef - Characteristics and Factors Associated with Cognitive Decline of Elderly with Mild Cognitive Impairment
Eul Hee Roh
Journal of Health Informatics and Statistics.2023; 48(3): 179. CrossRef - Detection and Intervention of Subjective Cognitive Decline in Pre-Alzheimer’s Disease
雅红 何
International Journal of Psychiatry and Neurology.2022; 11(04): 65. CrossRef - Influencing Factors of Subjective Cognitive Impairment in Middle-Aged and Older Adults
Min Roh, Hyunju Dan, Oksoo Kim
International Journal of Environmental Research and Public Health.2021; 18(21): 11488. CrossRef
- Cognitive Dysfunction Prediction Model with Lifelog Dataset based on Random Forest and SHAP
- 324 View
- 3 Download
- 2 Web of Science
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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
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Abstract
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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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Citations
Citations to this article as recorded by- Stroke knowledge and health-promoting behaviors: Mediating effect of patient self-esteem
GyeongChae MUN, JaeLan SHIM
Patient Education and Counseling.2024; 129: 108398. CrossRef - Comparison of Stroke Knowledge, Health Beliefs, and Stroke Prevention Behavior between Early and Middle-Aged Adults
Eun Ko
STRESS.2022; 30(2): 98. CrossRef - Variation in Knowledge of Stroke Warning Signs by Age and Presence of Conventional Risk Factors
Juyeon Oh, Hyun Young Kim, Young Seo Kim, Sun Hwa Kim
Journal of Cardiovascular Nursing.2022; 37(2): 177. CrossRef - Analysis of Subgroups with Lower Level of Patient Safety Perceptions Using Decision-Tree Analysis
Sun Hwa Shin
Journal of Korean Academy of Nursing.2020; 50(5): 686. CrossRef - Stroke Management Awareness and Behavior among Nursing Students in Bangladesh
Shariful Islam, Eui Geum Oh, Tae Wha Lee, Sanghee Kim
Open Journal of Nursing.2017; 07(01): 1. CrossRef - Awareness of Stroke Warning Symptoms and Related Factors among Residents in a Province
Yu-Mi Lee, Keon-Yeop Kim, Ki-Su Kim
Journal of the Korea Academia-Industrial cooperation Society.2014; 15(8): 5116. CrossRef
- Stroke knowledge and health-promoting behaviors: Mediating effect of patient self-esteem
- 148 View
- 5 Download
- 6 Crossref
- 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
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Abstract
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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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Citations
Citations to this article as recorded by- Influence of Different Exercise Types on Health-Related Quality-of-Life in Men With Depressive Disorder in South Korea
Kyungjin Kim, Kyo-Man Koo
Frontiers in Public Health.2022;[Epub] CrossRef - Factors related to Depression of Rural Elders
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
Doonam Oh, Chul-Gyu Kim
Korean Journal of Adult Nursing.2015; 27(5): 583. CrossRef - Risk Factors Influencing Depression among Elderly with Arthritis in Korean
Hye-Ryoung Kim, Hyun-Hee Im
The Korean Journal of Health Service Management.2014; 8(1): 103. CrossRef
- Influence of Different Exercise Types on Health-Related Quality-of-Life in Men With Depressive Disorder in South Korea
- 84 View
- 1 Download
- 4 Crossref
- 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
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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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Citations
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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
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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
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Physical Therapy Rehabilitation Science.2022; 11(3): 285. CrossRef - Occupational accident prediction modeling and analysis using SHAP
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Journal of Digital Contents Society.2021; 22(7): 1115. CrossRef - FACTORS DETERMINING THE EXTENT OF GDPR IMPLEMENTATION WITHIN ORGANIZATIONS: EMPIRICAL EVIDENCE FROM CZECH REPUBLIC
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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
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Journal of Korean Medical Science.2019;[Epub] CrossRef - Factors Influencing Depression in Middle Aged Women: Focused on Quality of life on Menopause
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Journal of Health Informatics and Statistics.2018; 43(2): 148. CrossRef - Song-Induced Autobiographical Memory of Patients With Early Alzheimer's Dementia
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Journal of Music and Human Behavior.2016; 13(2): 49. CrossRef - Factors Affecting on Life Satisfaction of Elderly after Total Knee Arthroplasty
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Journal of Digital Convergence.2016; 14(9): 563. CrossRef - Application of big data analysis with decision tree for the foot disorder
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Cluster Computing.2015; 18(4): 1399. CrossRef - A Study on Comparison of Classification and Regression Tree and Multiple Regression for Predicting of Soldiers' Depression
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Healthcare Informatics Research.2014; 20(1): 30. CrossRef - The predictability of dentoskeletal factors for soft-tissue chin strain during lip closure
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