• Animal Systematics, Evolution and Diversity
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• v.29 no.2
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• pp.136-143
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• 2013

The collared scops owl that occurs in Korea is a protected species but its exact specific status has been questioned. To resolve the species status, a molecular phylogenetic analysis was conducted using two fragments of mitochondrial DNA, cytochrome b (cyt b, 891 bp) and NADH dehydrogenase subunit 2 (ND2, 627 bp) genes. Phylogenetic trees of cyt b revealed that all Korean specimens formed a monophyletic group with Japanese scops owl Otus semitorques with very low sequence divergence (d=0.008). We obtained a similar ND2 tree as well (d=0.003); however, the genetic distance between Korean individuals and O. lempiji from GenBank (AJ004026-7, EU348987, and EU601036) was very high and sufficient enough to separate them as species (cyt b, d=0.118; ND2, d=0.113). We also found that Korean species showed high differentiation from O. bakkamoena (AJ004018-20 and EU601034; cyt b, d=0.106; ND2, d=0.113) and O. lettia (EU601109 and EU601033, cyt b, d=0.110; ND2, d=0.117) as well. Therefore, we suggest that the Korean collared scops owl should be designated as Otus semitorques.

• Korean Journal of Community Nutrition
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• v.19 no.5
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• pp.468-478
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• 2014

Objectives: Intake of fruits and vegetables has protective effects against stroke attack. This study intended to examine the status of consuming fruits and vegetables and to find out which factors may influence the frequency of consumption of fruits and vegetables in individuals with a history of stroke. Methods: The data of 208 subjects from 5th (2010, 2011) Korea National Health and Nutrition Examination Survey (KNHNES) who reported a stroke diagnosis was used for analysis. To identify major factors influencing the consumption of fruits and vegetables, a classification-tree analysis was carried out. Results: Among those who reported a stroke diagnosis, the frequencies of consumption of fruits and vegetables were influenced by their age, place of residence (urban or rural), economic status, educational level, occupation, number of family members, frequency of eating out, and having meals (breakfast or lunch) with family members. Two factors from fruits and three factors from vegetables were generated by exploratory factor analyses. Urban residents ate fruits and vegetables more frequently in all factors than rural residents. Eating frequencies of 'seasonal fruits (orange, apple, strawberry, melon, pear and watermelon)', 'easily-accessible fruits (persimmon, tangerine, grape, peach, banana)', and 'Western-style vegetables (cabbage, mushroom, carrot, tomato, spinach)' were influenced by the socioeconomic status. Eating frequencies of 'Korean-style vegetables (bean sprout, radish leaves, pumpkin/squash, sea weed)', 'preserved vegetables (Korean cabbage, radish, laver, cucumber)' were influenced by having breakfast with family members. Conclusions: The results of this study suggested that by eating more fruits and vegetables, more preventive effects against secondary stroke attack are expected in stroke patients who live in the rural areas and who do not eat breakfast with family members. In addition, more outreach and education programs are needed for them.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• Journal of the Korean Society of School Health
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• v.19 no.1
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• pp.79-88
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• 2006

Purpose : To identify general characteristics of school personnel with recent fatigue which was the most frequent symptom among subjective symptoms and to explore fatigue-related factors by evaluating physical and perceived health status, life style, and symptoms through data mining techniques. Methods : We collected a data of the 1,147(male 545, female 602) who were elementary, middle, or high school personnel, answered a questionnaire, and received physical examination in Seoul School Health Center from September to November in 2000. And we investigated the differences between fatigue group and non-fatigue group for demographic characteristics, physical health status, perceived health status, symptoms, and laboratory values by frequency, chi-square test, t-test, or simple logistic regression analysis by SAS package 8.1, and then selected significant variables as input variables of a decision tree analysis of CART model by SAS E-miner. Results : In general characteristics, the fatigue consisted of 41.1%(male 35.2%, female 46.4%) among 1,147 school personnel. In classical statistics, factors related with fatigue were female, lower means of systolic and diastolic pressure, young age, personnel in middle school, irregular eating habit, no exercise a week or less than 30minutes exercise a day, perception of unhealthy status, and subjective symptoms including short of breath at exercise. In simple logistic regression to examine the relationship between selected independent variables and fatigue as a dependent variable, the odds ratio of gender (female vs male) was 1.58 times, and young age ( 20s vs 60s) 20.67 times, and middle vs high school personnel 1.86 times. However, we mined combined several characteristics by SAS-E miner. In CART model, if health perception was healthy, and age was >= 37.5 years, the proportion of the fatigue was only 19.3%. but if health perception was not healthy and symptom was severe 'short of breath' during exercise and age was < 53.5 years, and BMI was >= 22.69, the proportion of the fatigue was up to 84.8%. Conclusions : The fatigue consisted of 41.1%(male 35.2%, female 46.4%). In classical statistics, fatigue-related factors among school personnel were young age, female gender, perceived unhealthy status, subjective physical symptoms, poor life-style, and lower blood pressure rather than only physical health status. However, in data mining, if health perception was healthy and age was >= 37.5 years, the proportion of the fatigue was only 19.3%. but if health perception was not healthy and symptom was severe 'short of breath' during exercise and age was < 53.5 years, and BMI was >= 22.69, the proportion of the fatigue was up to 84.8%.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.265-279
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• 2018

This study conducted an experiment using data mining techniques to develop prediction models of worker job turnover. The experiment used data from the '2015 Graduate Occupational Mobility Survey' by the Korea Employment Information Service. We developed the prediction models using a decision tree, Bayes net, and artificial neural network. We found that the decision tree-based prediction model reported the best accuracy. We also found that the six influential factors affecting employees' turnover intention are type of working time, job status, full-time or not full-time, regular working hours per week, regular working days per week, and personal development opportunities. From the decision tree-based prediction model, we derived 12 rules of employee turnover for all job types. Using the derived rules, we proposed helpful directions for enhancing workers' job tenure. In addition, we analyzed the influential factors affecting employees' job turnover intention according to four job types and derived rules for each: office (ten rules), culture and art (nine rules), construction (four rules), and information technology (six rules). Using the derived rules, we proposed customized directions for improving the job tenure for each group.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.63-69
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• 2018

This study is to examine the determinants of the elderly poverty by using the Decision-tree analysis. In line with this perspective, this study includes individual characteristics, family characteristics, working characteristics, and periodic income characteristics after retirement as determinants for senior poverty. The study uses data from the Korean Retirement and Income Study based on panel survey and employs the Decision-tree analysis to explain the causes of the elderly poverty. As the result of analysis, earned wage has the greatest effect on the elderly poverty. Depending on status of the earned wage, there are 2 different variable groups. One with no earned wage includes public pension, education, and residence, paid employee and gender in the other with earned wage. Based on the analytical results, the study suggests measures to address the elderly poverty.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.165-172
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• 2015

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.575-583
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• 2003

We proposed imputation strategies for the Population and Housing Census 2000 in Korea. The total area of floor space and marital status which have relatively high non-response rates in the Census are considered to develope the effective missing value imputation procedures. The Classification and Regression Tree(CART) is employed to construct the imputation cells for hot-deck imputation, as well as to predict missing value by model-based approach. We compare three imputation methods which include CART model-based imputation, hot-deck imputation based on CART and logical hot-deck imputation proposed by The Korea National Statistical Office. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable.

• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.4
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• pp.185-193
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• 2002

With Increasing awareness towards environment-friendly and non-toxic pesticide azadirachtin obtained from neon tree (Azadirachta indica) is gaining more and more importance. Its broad-spectrum activity, Peculiar mode of action. eco-friendly and non-toxic action towards beneficial organisms has offered many advantages over chemical pesticides. All currently use commercial formulations based on azadirachtin contains azadirachtin extracted from seeds of naturally grown whole plants which is labour intensive process depending upon many uncontrollable geographical and climatic factors. Plant tissue culture can be a potential process for the pro-duction, offering consistent, stable and controlled supply of this bioactive compound, However the research on tissue culture aspects of production are in preliminary stage and requires culture and process optimization for the development of a commercially viable process. This review states the present status and future challenges of plant tissue culture for azadirachtin production.