• Journal of Digital Convergence
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• v.19 no.12
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• pp.477-486
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• 2021

This study was conducted to detect metabolic syndrome risk factors and gender difference in adults. 18,616 cases of adults are collected by Korea Health and Nutrition Examination Study from 2016 to 2019. Using 4 types of machine Learning(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest) to predict Metabolic Syndrome. The results showed that the Random Forest was superior to other methods in men and women. In both of participants, BMI, diet(fat, vitamin C, vitamin A, protein, energy intake), number of underlying chronic disease and age were the upper importance. In women, education level, menarche age, menopause was additional upper importance and age, number of underlying chronic disease were more powerful importance than men. Future study have to verify various strategy to prevent metabolic syndrome.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.73-82
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• 2022

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.

• The Korean Journal of Ecology
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• v.7 no.4
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• pp.185-193
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• 1984

Regneration process of Abies koreana-Pinus koraiensis community consisted of a subalpione coniferous forest in Mt. Jiri, Korea, was studied in relation to age structure, especially to gap formation. The tall-tree layer (ca. 6.5m) is dominated by Abies koreana and Pinus koraiensis, while lower layer by the sapling and juveniles of A. koreana and Picea jezoensis below 2m tall. The ranges of DBH in A. koreana, P. koraiensis and P. jezonesis were 11.8cm~26.4m, 11.7cm~24.5 cm and 18.2cm~21.7 cm, respectively. The trees below 130 cm tall had contagious distribution, while tall and subtall trees had uniform distribution. The average tree ages of A. koreana, P. koraieniensis and P. jezoensis were 60~70 years, 70~80 years and 70~90 years, respectively. The saplings and juveniles below 20 years in tree ages were occupied over 80% of total trees. The coniferous trees in the gaps or around dead trees were composed of sapligs and juveniles which had emerged or invaded about 20 years before and after the gap formation. The Betula type regeneration of the coniferous forest took place in gaps which orginated from the failing down of a single or a few trees by longevity. Accordingly, it is clear that the subalpine coniferous forest composed of A. koreana of A. koreana, P. koraiensis and P. jezoensis in Mt. jiri was supporting by the regeneration pattern of Betula type.

• Korean Journal of Childcare and Education
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• v.16 no.5
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• pp.69-86
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• 2020

Objective: The purpose of this research was to examine the prediction models of conflict and intimacy in teacher-child relationships based on decision tree analysis. Methods: The participants were 297 preschool children from ages three to five including 166 boys and 131 girls. Teacher-child relationships were measured by the Student-Teacher Relationship Scale(STRS). Physical aggression, relational aggression, social withdrawal, and prosocial behaviors were measured by teacher ratings. Moreover, ADHD-RS(Attentive Deficit Hyperactivity Disorder Rating Scale) was used to measure ADHD. The data was analyzed with decision tree analysis. Results: According to the prediction model for teacher-child conflict, the significant predictors were physical aggression and social withdrawal. According to the prediction model for teacher-child intimacy, the significant predictors were prosocial behaviors and relational aggression. However, children's age, gender and ADHD were not significant predictors. Conclusion/Implications: The findings suggest that social behaviors may be closely related with teacher-child relationships for preschool children. Based on the results of this study, intervention suggestions were made.

• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.

• Journal of Wetlands Research
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• v.4 no.2
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• pp.1-11
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• 2002

The morphology, seed germination, and life history of Salix nipponica were investigated in the wetland of the Nam-River, Jinju, Gyeongsangnam-do, Korea from 2000 to 2001 to provide the basic data necessary for riverine ecosystem restoration through understanding the growth characteristics of Salix nipponica. Salix nipponica had stomata on only lower side and stomata type was paracytic. Salix nipponica produced many small and light seeds. The seed number per mature ament was 1599.4, seed mass of 0.04 mg, and fertilization rate of 66.1%. Seed germination was little affected by light. Germination rate was high and mean germination time was short Under flooding condition, seeds were germinated normally and were viable after as much as 14 days of flooding. But there were no differences under various water depths on germination rates. Tree age was closely correlated with more stem diameter than tree height and there was no difference of growth rate between male and female tree. Growth rate was most rapid for 2 to 3 years after germination, and length growth was almost stopped for more than 11 years even though mass growth was done. Besides even a second-year indivisual was flowered.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.1-7
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• 2021

The number of new students at many domestic universities is declining. In particular, private universities, which are highly dependent on tuition, are experiencing a crisis of existence. Amid the declining school-age population, universities are striving to fill new students by improving the quality of education and increasing the student employment rate. Recently, there is an increasing number of cases of using the accumulated big data of universities to prepare measures to fill new students. A representative example of this is the analysis of factors that affect student employment. Existing employment-influencing factor analysis studies have applied quantitative models such as regression analysis to university big data. However, since the factors affecting employment differ by major, it is necessary to reflect this. In this paper, the factors affecting employment by major are analyzed using the data of University C and the decision tree model. In addition, based on the analysis results, a roadmap for student employment by major is recommended. As a result of the experiment, four decision tree models were constructed for each major, and factors affecting employment by major and roadmap were derived.

• Advances in concrete construction
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• v.14 no.1
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• pp.15-34
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• 2022

The use of waste rubber in concrete can reduce natural aggregate consumption and improve some technical properties of concrete. Although there are several equations for estimating the mechanical properties of concrete containing waste rubber, limited numbers of machine learning-based models have been proposed to predict the mechanical properties of rubbercrete. In this study, an extensive database of the mechanical properties of rubbercrete was gathered from a comprehensive survey of the literature. To model the mechanical properties of rubbercrete, M5P tree and linear gene expression programming (LGEP) methods as two machine learning techniques were employed to achieve reliable mathematical equations. Two procedures of input variable selection were considered in this study. The crucial component ratios of rubbercrete and concrete age were assumed as the input variables in the first procedure. In contrast, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber were considered the second procedure of the input variables. The results show that the models obtained by LGEP are more accurate than those achieved by the M5P model tree and existing traditional equations. Besides, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber are better predictors of the mechanical properties of rubbercrete compared to the first procedure of input variable selection.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.79-84
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• 2023

Social studies show that the number of separated families have lately increased due to different reasons. Despite the causes for family rift, many problems are resulted which affected the children physically and psychologically. This effect may cause them fail in their life especially at school. This paper focuses on the negative reaction of the parents' separation with other factors from the computer science prospective. Since the artificial intelligent field is the most common widespread in computer science, a predictive model is built to predict if a specific child whose parents separated, may complete the school successfully or fail to continue his education. This will be done using Decision Tree that have proved their effectiveness on the predication applications. As an experiment, a sample of individuals is randomly chosen and applied on our prediction model. As a result, this model shows that the separation may cause the child success at school if other factors are satisfied; the intelligent of the guardian, the relation between the parents after the separation, his age at the separation time, etc.

• The Journal of Korean Society for School & Community Health Education
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• v.24 no.1
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• pp.47-59
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• 2023

Objectives: The aim of this study was to identify risk factors of alcohol use disorder among Korean adults. Methods: Cross-sectional exploratory study based on data collected from Data from the 6th Korea National Health and Nutrition Examination Survey in 2015 were performed in this study. There were 3,248 participants who were 2,558 normal drinkers while 690 had alcohol use disorder. Decision tree analysis were used to exam socio-demographic and health-related factors to predict alcohol use disorder. Results: As a result of decision tree analysis, the predictive model for factors related to alcohol use disorder in Korean adults presented with 8 pathways. The significant predictors of alcohol use disorder were age, gender, smoking, marital status, and house income. Male smokers whose household income is 'high' or 'low' are most vulnerable to alcohol use disorders. Conclusions: This study indicates that need to consider health behavior and house income when we practice prevention policies and health education of alcohol use disorder.