• 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.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.75-81
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• 2007

The need for the study of the revealing Sasang constitution at scientific term is increasing as the application of this discipline to the patient produces more accurate result. To obtain scientific evidence of Sasang constitution, it is crucial to analyze accumulated clinical information and associate them to the biological indices that may classify Sasang constitution. Thus, the analysis of clinical information is the most important stepping stone to go toward to the stage of developing model and decision support system (DSS) for classifying Sasang constitution. This study is a preliminary analysis of 1,109 samples collected with 171 clinical indices. To find meaningful clinical indices for classifying Sasang constitutional medicine, we applied decision tree model for them. The skin of 66.5% within whole Taeeumin is thick and non feeble. In the case of 69.8% within whole Soyangin, the skin is non feeble and slippery. In the case of 64.4% within whole Soeumin. they have feeble skin. Therefore, the property of skin can be suggested to be more important than any other index for the classification of Sasang constitution.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.881-893
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• 2012

This paper proposes to develop system trading strategy using rough set, decision tree in futures market. While there is a great deal of literature about the analysis of data mining, there is relatively little work on developing trading strategies in futures markets. There are three objectives in this paper. The first objective is to analysis performance of decision tree in rule-based system trading. The second objective is to find proper profitable trading interval. The last objective is to find optimized training period of trading rule training. The results of this study show that proposed model is useful trading strategy in foreign exchange market and can be desirable solution which gives lots of investors an important investment information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.311-319
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• 2015

In this paper, in order to increase the production efficiency of the industrial plant, and predicts the resources of the manufacturing process, we have proposed a decision-making system for resource implementing the risk management effectively forecasting and risk management. A variety of information that occurs at each step efficiently difficult the creation of detailed process steps in the scenario you want to manage, is a frequent condition change of manufacturing facilities for the production of various products even within the same process. The data that is not contiguous products production cycle also not constant occurs, there is a problem that needs to check the variation in the small amount of data. In order to solve these problems, data centralized manufacturing processes, process resource prediction, risk prediction, through a process current status monitoring, must allow action immediately when a problem occurs. In this paper, the range of change in the design drawing, resource prediction, a process completion date using a regression algorithm to derive the formula, classification tree technique was proposed decision system in three stages through the boundary value analysis.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.1
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• pp.41-58
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• 2016

Reliability Centered Maintenance (RCM) introduces a systematic method and decision logic tree for utilizing previous operating experience focused on reliability and optimization of maintenance activities. In this paper RCM methodology is applied on safety injection system for APR-1400. Functional Failure Mode Effects and Criticality Analysis (FME&CA) are applied to evaluate the failure modes and the effect on the component, system and plant. Logic Tree Analysis (LTA) is used to determine the optimum maintenance tasks. The results show that increasing the condition based maintenance will reduce component failure and improve reliability and availability of the system. Also the extension of the surveillance test interval of Safety Injection Pumps (SIPs) would lead to an improved pump's availability, eliminate the unnecessary maintenance tasks and this will optimize maintenance activities.

• Journal of the Korea Institute of Building Construction
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• v.11 no.2
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• pp.100-107
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• 2011

As construction duration significantly influences the performance and the success of construction projects, it is necessary to appropriately manage the impact factors affecting construction duration. Recently, interest in the construction industry has been rising due to the recent change in the construction legal system, and the competition among the construction companies on construction time. However, the impact factors are extremely diverse. The existing productivity data on impact factors is not sufficient to properly identify the impact factor and measure the productivity from various perspectives, such as subcontractor, time, crew, work and so on. In this respect, a multidimensional analysis by a data warehouse is very helpful in order to view the manner in which productivity is affected by impact factors from various perspectives. Therefore, this research proposes a method that effectively takes the diverse productivity data of impact factors, and generates a multidimensional analysis. Decision tree analysis, a data mining technique, is also applied in this research in order to supply construction managers with appropriate productivity data on impact factors during the construction management process.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.157-165
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• 2002

Since 1997, when a new educational system that encourages faculties instead of departments in universities is first introduced, students have much more chance to choose and leave their majors than before. As a result, colleges of basic arts and sciences confront with a serious problem since lots of students have left their majors at the colleges. In this paper, we analyze and provide a predictive model for those students in a university using decision trees.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• Asia Marketing Journal
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• v.20 no.2
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• pp.65-83
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• 2018

Technological advances are bringing autonomous vehicles to the ever-evolving transportation system. Anticipating adoption of these technologies by users is essential to vehicle manufacturers for making more precise production and marketing strategies. The research investigates regulatory focus and consumer innovativeness with consumers' adoption of autonomous vehicles (AVs) and to consumers' subsequent willingness to pay for AVs. An online questionnaire was fielded to confirm predictions, and regression analysis was conducted to verify the model's validity. The results show that a promotion focus does not have a significantly positive effect on the automation level at which consumers will adopt AVs, but a prevention focus has a significantly positive effect on conditional AV adoption. Consumer innovativeness, consumers' novelty-seeking have a significantly positive relationship with high and full AV adoption, and consumers' independent decision-making has a significantly positive effect on full AV adoption. The higher the level of automation at which a consumer adopts AVs, the higher the willingness to pay for them. Finally, using a neural network and decision tree analyses, we show methods with which to describe three categories for potential adopters of AVs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.5
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• pp.46-52
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• 2002

Today's database system needs to collect huge amount of questionnaire that results from development of the information technology by the internet, so it has to be administrable. However, there are many difficulties concerned with finding analytic data or useful information in the high capacity-database. Data mining can solve these problems and utilize the database. Questionnaire analysis that uses data mining has drawn relevant patterns that did not look or was tended to overlook before. These patterns can be applied by a new business rule. The purpose of this research is to analyze the questionnaire results and to present the result that can help to make decision easily with data mining. Recognition and analysis about these techniques of data mining show suitable type of questionnaire survey. This research focus on the form of present composition and the model of suitable questionnaire to analyze the type of it. Also, the comparison between the actual questionnaire result and the conventional statistical analysis is examined.