• Journal of Digital Convergence
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• v.15 no.5
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• pp.505-515
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• 2017

This study examines the verification of algorithm on resilience leisure programs for the productive aging of the new elderly in Korea. The subjects for this study were 525 new elderly who lived in metropolis, medium-sized cities and farming area. The reliability and validity test of the questionnaire were conducted by using SPSS 20.0 program; the results of tree analysis are as follows; First, The influential factor in the resilience leisure programs is subjective health status, desire for activity, interpersonal exchange and household income. The most influential resilience factor of algorithm is interpersonal relationship, self-regulating and affirmative. The structural algorithm of resilience was that low interpersonal relationship group related to the affirmative and high interpersonal relationship group related to the self-regulating.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.80-93
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• 2024

This study attempted to analyze the factors that influence the participation of beneficiary companies in the government's defense industry promotion support project. To this end, experimental data were analyzed by constructing a prediction model consisting of highly important variables in beneficiary company decisions among various company information using the decision tree model, one of the data mining techniques. In addition, various rules were derived to determine the beneficiary companies of the government's support project using the analysis results expressed as decision trees. Three policy measures were presented based on the important rules that repeatedly appear in different predictive models to increase the effect of the government's industrial development. Using the analysis methods presented in this study and the determinants of the beneficiary companies of the government support project will help create a sustainable future defense industry growth environment.

• Journal of Korean Academy of Nursing
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• v.43 no.3
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• pp.442-451
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• 2013

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${\geq}$ 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 pathways. 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.

• Journal of Korean Academy of Nursing
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• v.40 no.3
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• pp.378-388
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• 2010

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$\acute{e}$, 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.

• Child Health Nursing Research
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• v.25 no.2
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• pp.95-102
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• 2019

Purpose: The purpose of this study was to identify factors associated with career maturity among Korean high school students. Methods: A descriptive cross-sectional design was adopted using secondary data from the 2012 Korean Welfare Panel Study (KoWePS). The participants were 496 high school students who completed the supplemental survey for children, which included items on career maturity, self-esteem, study stress, teacher attachment, relationship with parents, peer attachment, depression and anxiety. Descriptive statistics, the chi-square-test, the t-test, and a decision tree were used for data analysis. Results: The decision tree identified five final nodes predicting career maturity after forcing self-esteem as the first variable. The highest predicted rate of high career maturity was associated with high self-esteem, experience of career counseling, and high teacher attachment. The lowest predicted rate of high career maturity was associated with low self-esteem and low attachment to friends. Conclusion: Factors influencing career maturity were varied by levels of self-esteem in Korean high school students. Thus, it is necessary to develop different approaches to enhance career maturity according to levels of self-esteem.

• Journal of Korean Public Health Nursing
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• v.36 no.2
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• pp.157-169
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• 2022

Purpose: This study aimed to implement a model for predicting the presence or absence of suicidal ideation in adolescents by using the decision tree analysis method. Methods: This study is a secondary data analysis using the 2019 Child and Adolescent Human Rights Survey, the most recent data published by the Korea Youth Policy Institute. In order to identify the variables predicting suicidal ideation, a decision tree analysis with suicidal ideation as a dependent variable was performed. Results: This study found that the variables of life satisfaction, insults from parents, sex, and cyber-bullying experience of adolescents were selected as significant predictors of suicidal ideation. It is predicted that 58.2% of subjects with low life satisfaction would think of suicide. Among them, the probability of thinking of suicide increased to 72.7% in the case of unhappy people, and the probability of thinking of suicide in the case of a woman increase to 82.9%. Conclusions: It is necessary to consider family, school, and society environment to prevent suicidal ideation of adolescents.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.303-303
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• 2022

기후변화로 인해 기상이변 현상의 발생 빈도가 잦아지며 가뭄 방생 빈도 또한 증가하는 추세이다. 이에 따라 가뭄 피해를 경감하는 선제적 가뭄대응체계 구축과 가뭄이 발생한 이후에 피해를 최소화하기 위한 연구가 필요하다. 본 연구에서는 가뭄피해 여부를 이진분류 방법으로 접근하여 예측 가능성을 검토하였다. 가뭄피해 여부는 비상급수(제한급수,운반급수) 자료를 이용하여 비상급수가 시행된 경우를 가뭄피해 발생으로 보고, 비상급수가 시행되지 않은 경우를 피해 없는 사례로 구분하였다. 기상 상황 변수로는 강수량, 기온, 상대습도 등을 이용하였다. 또한 지역별 연간 총 급수량 대비 저수량을 이용하여 지역별 현 상황을 고려하고자 하였다. 의사결정나무를 이용하여 분석한 결과 불균형 클래스 문제의 정확도에 주로 이용되는 오차행렬의 정확도가 0.95 이상으로 나타났으며, F1-Score는 약 0.5 로 나타났다. 이는 예측 결과 전체를 대상으로 했을 경우 95 %의 확률로 가뭄피해 여부를 구분할 수 있는 것을 나타내며, 가뭄 피해만을 대상으로 했을 경우 50 %의 정확도를 타나낸다. 그러나 본 연구에서는 비상급수를 유발하는 충분한 환경적 변수를 고려하지 않았고, 다양한 딥러닝 모형을 분석하지 않았다. 따라서 비상급수를 유발하는 요인을 충분히 고려하고 딥러닝 기법을 고도화 한다면 모형의 정확도 개선을 기대할 수 있을 것으로 판단된다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.419-419
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• 1996

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.63-71
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• 2016

The purpose of this study is to investigate factors affecting dropout intention based on education satisfaction survey analysis of local J university. Total 7,248 survey data which has high trustability were analyzed. Analysis of variance was performed to verify differences between each grade and credits level. There are significant differences between the year grade and credit level. Especially the result show that the satisfaction of freshman is higher than the other grade students. To verify relation between intention to dropout and satisfaction of university education logistic regression analysis method has been applied and satisfaction of academic guidance, vocational guidance, environment of education and self-satisfaction of university life are significantly related to the dropout intention. The most important variable is self-satisfaction of university life which determine dropout intention through decision tree analysis.

• Informatization Policy
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• v.25 no.2
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• pp.20-45
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• 2018

This study aims to find the factors of Internet overdependence in adults, through the decision tree analysis model, which is a data mining method using National Information Society Agency's raw data from the survey on Internet overdependence in 2016. As a result of the decision tree analysis, a total 16 nodes of Internet overdependence risk groups were identified. The main predicated variables were the amount of time spent per smart media usage in weekdays; amount of time spent per smart media usage in weekends; experiences of purchasing cash items; percentage of using smart media for leisure; negative personality; percentage of using smart media for information search and utilization; and awareness on good functions of the Internet, all of which in order had greater impact on the risk groups. Users in the highest risk node spent the smart media for more than 5 minutes per use and less than 5~10 minutes in weekdays, had experiences of cash item purchase, and had lower level of awareness on the good functions of the Internet. The analysis led to the following recommendations: First, even a short-time use has higher chances of causing Internet overdependence, and therefore, guidelines need to be developed based on research on the usage behavior rather than the usage time. Second, self-regulation is required because factors that affect overindulgence in games, such as the cash items, increase Internet overdependence. Third, using the Internet for leisure causes higher risk of overdependence and therefore, other means of leisure should be recommended.