• 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.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.235-242
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• 2006

Many researches have achieved to reuse existent experience at each step of software life cycle. It is achieved various study about Design Pattern serving that abstract level solves design problem at high level. But, design step is not supplying design information that is abstracted so that new designers may reuse standardizing defined good design information. Therefore, in this paper, to do so that can reuse existent design information because composes Design Pattern can solve problem at design step, and extend design information that also is standardized XMI to base meta model of Design Pattern. And I did so that can compose Design Pattern utilizing MetaModel of Design Pattern standardized by XMI. Therefore, software designer can reuse composing existent design information that is could standardize problem at design because uses MetaData of standardized Design Pattern, and designs by Design Pattern base.

• The Journal of Information Systems
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• v.16 no.4
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• pp.131-155
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• 2007

The purpose of this thesis is to study on the philosophical analysis model and its methodological application of information systems research evaluation from critical realist perspective. Fist of all, I examine ontological epistemological methodological assertions of critical realism. Because the philosophy of critical realism is an opportunity for information systems study. I examine Dobson and Mutch's critical realist perspective on actors-structure model. I suggest a critical realist actors-praxis-structure model. This model provides the potential for a new approach to social investigations in its provision of an ontology for the analytical separation of structure and agency. Of most importance might be the incorporation of non-humans into the analysis of social interaction and of technology into the elaboration of structures. I also examine Tsoukas's critical realistic meta-theory of management. I suggest a critical realist IS management model. This model elucidate the nature of management and delineate the scope of applicability of various perspectives on management. The causal powers of management reside in the real domain and, taken together, their logics are contradictory, the effects of their contradictory composition are contingent upon prevailing contingencies. I analyze Carlsson's theory of design knowledge. His framework builds on that the aim of IS design science research is to develop practical knowledge for the design and realization of different classes of IS initiatives, where IS are viewed as socio-technical systems and not just IT artefacts. The framework proposes that the output of IS design science research is practical IS design knowledge in the form of field-tested and grounded technological rules. The IS design knowledge is developed through an IS design science research cycle. In conclusion, I think that IS actors-praxis-structure model, meta-theoretical IS management model, and IS design knowledge model according to critical realistic approach are very useful for IS research evaluation. Nevertheless, important problems are left not resolved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.921-929
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• 2008

Recently, many researches have been conducted to solve data heterogeneity as a way for data integration. The elements of the system that we suggest are an XMDR wrapper and XMDR Repository. XMDR wrapper solves the heterogeneity of the existing system by creating the interface based on the standard information of XMDR, and performing the inter-conversion between global XMDR query and local query using mapping data on standard information and local schema. XMDR Repository are composed of XMDR which manages the mapping data on standard information and local schema, and of Proxy DB which saves the accomplished results. With XMDR wrapper and XMDR Repository, users can use the same interface, and they need not conduct repeated queries since XMDR wrapper not only solves the heterogeneity of the schema using the meta-semantic ontology of XMDR, but also considers the heterogeneity accompanying the meaning of the value through instance semantic ontology. Therefore, in this paper we suggest the grid wrapper for the solution of data heterogeneity and efficient data integration.

• Journal of the Society of Disaster Information
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• v.14 no.2
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• pp.203-210
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• 2018

Purpose: The purpose of this study is to build a model for the meta - evaluation of the national infrastructure system and to improve the evaluation system of the national infrastructure system using the model. Method: For the study, the disaster-related laws and regulations, the evaluation report of the national infrastructure system published by the government, the guideline for the establishment of the national infrastructure protection plan, the meta-evaluation previous research data, To analyze the actual state of the evaluation. Results: Among the indices of evaluation of the current national infrastructure system, the supplementary requirements were derived from seven indicators such as appropriateness of education and training plan and implementation of disaster response, evaluation and communication with stakeholders, and evaluation committee training time. Conclusion: It is expected that the improvement plan derived from this study can be used to improve the evaluation index of the national infrastructure system.

• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.39-49
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• 2004

An increasing number of distributed applications will be achieved with mobile technology. These applications face temporary loss of network connectivity when they move. They need to discover other hosts in an ad-hoc manner, and they are likely to have scarce resources including CPU speed, memory and battery power. Software engineers building mobile applications need to use a suitable middleware that resolves these problems and offers appropriate support for developing mobile applications. In this paper, we describe the meta-server building for middleware that addresses reflective context awareness and present usability with demonstration. Metadata is consist of user configuration, device configuration, user context, device context and dynamic image metadata. When middleware send a saving or retrieval request to meta-server, it returns messages to middleware after the verification of the request. This meta-server has the application for multimedia stream services with context awareness.

• The Journal of Information Systems
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• v.28 no.4
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• pp.293-311
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• 2019

Purpose This paper is to make a meta-analysis of the relationship between the social media use and learning performance as well as its potential moderating variables to clarify the differences in research conclusions in existing literatures, and refine the situational and method factors that affect the relationship between them. Methodology Meta-analysis used in this study can combine the quantitative data from different empirical studies, focus on the same research problem, and finally reach a research conclusion. Findings The results show that social media use and learning performance have a moderating positive correlation. The moderating effect test of usage scenarios shows that social media types, usage groups, application platforms and discipline fields have moderating effects on the relationship between social media use and learning performance. The moderating effect test of the research method found that measurement models, data attributes and learning performance indicators also had moderating effects on the relationship between social media use and learning performance.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.375-384
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• 2008

Computer programming has educational effect on improving high-level thinking abilities. However, students initially have to spend too much effort in learning the basic grammar and the usage model of programming languages, which negatively affects their eagerness in learning. To remedy this problem, we propose to apply the Scratch to a Game Developing Programming Class; Scratch is an easy-to-learn and intuitive Educational Programming Language (EPL) that helps improving the Meta-cognition and Self-efficacy of middle school students. Also we used the Demonstration-Practice instruction model with self-questioning method for activating the Meta-cognition. In summary, a game developing programming class using Scratch was shown to significantly improve the Meta-cognition of middle school students. However it was shown to insignificantly improve the Self-efficacy of girl students group.

• Journal of East-West Nursing Research
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• v.26 no.1
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• pp.1-16
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• 2020

Purpose: The purpose of this study was to evaluate the effectiveness of non-pharmacologic interventions for chronic nonspecific low back pain (CLBP) in adults aged 18-64 years. Methods: We searched for potentially relevant randomized controlled trials and non-randomized controlled trials through five Korean electronic databases (i.e., Korean Studies Information Service System, Research Information Sharing Service, Korean Medical Database, KoreaMed, and National Assembly Library) published from January 2010 to May 2019. Two investigators independently selected the studies based on the criteria and assessed risk of bias in the included studies. We estimated the effect size of interventions using Comprehensive Meta Analysis 3.3. Results: Of 10,151 studies, 26 studies met the inclusion criteria and 15 studies were included in the meta-analysis. Exercise reduced low back pain (Hedges's g=-1.53, 95% CI: -2.22 to -0.85) and pain-related disabilities (Hedges's g=-0.92, 95% CI: -1.40 to -0.45). We found that taping was effective in decreasing low back pain (Hedges's g=-1.12, 95% CI: -1.51 to -0.73) and pain-related disabilities (Hedges's g=-0.50, 95% CI: -0.93 to -0.07). Manual therapy yielded a marginally significant reduction in low back pain (Hedges's g=-2.32, 95% CI: -4.64 to 0.00), the therapy was not effective in decreasing pain-related disabilities. Conclusion: Although there was little evidence for the effectiveness of manual therapy in adults with CLBP, exercise and taping were effective to relieve pain and pain-related disabilities. Based on these findings, we suggest the development of non-pharmacologic interventions or a nursing intervention protocol for the CLBP management. Also, nurses should consider implementation of effective non-pharmacologic interventions for CLBP.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.85-92
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• 2019

Companies can identify user groups or consumption trends by collecting and analyzing opinions of many users on special subjects or their products as well as utilize them as various purposes such as predicting some specific trends or marketing strategies. Therefore current analyzing tools of social media have come into use as a means to measure the performances of social media marketing through network's statistical analysis. However these tools require expensive computing and network resources including burden of costs for building up and operating complex software platforms and much operating know-how. Hence, small companies or private business operators have difficulty in utilizing those social media data effectively. This paper proposes a framework applied into developing analysis system of social media. The framework could be set up and operate the system to extract necessary social media's data. Also to design the system, this study suggests a meta-model of proposed framework and to guarantee completeness and consistency, a formal specification of meta-model by using Z language is suggested. Finally, we could verify the clearness of framework's design by performing Z model checking of formal specification's output through Z-EVES tool.