• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• Korean Journal of Adult Nursing
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• v.17 no.3
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• pp.411-424
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• 2005

Purpose: The purpose of this study was to develop and evaluate e-EBPP(Evidence-based Practice Protocol) system for nursing care for patients with dementia to facilitate the best evidence-based decision in their dementia care settings. Method: The system was developed based on system development life cycle and software prototyping using the following 5 processes: Analysis, Planning, Developing, Program Operation, and Final Evaluation. Result: The system consisted of modules for evidence-based nursing and protocol, guide for developing protocol, tool for saving, revising, and deleting the protocol, interface tool among users, and tool for evaluating users' satisfaction of the system. On the main page, there were 7 menu bars that consisted of Introduction of site, EBN info, Dementia info, Evidence Based Practice Protocol, Protocol Bank, Community, and Site Link. In the operation of the system, HTML, JavaScript, and Flash were utilized and the content consisted of text content, interactive content, animation, and quiz. Conclusion: This system can support nurses' best and cost-effective clinical decision using sharable standardized protocols consisting of the best evidence in dementia care. In addition, it can be utilized as an e-learning program for nurses and nursing students to learn use of evidence based information.

• Journal of Radiation Protection and Research
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• v.29 no.1
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• pp.65-71
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• 2004

A multi-attribute utility analysis was investigated as a tool for the decision support of countermeasures in early phase of a nuclear accident. The utility function of attributes was assumed to be the second order polynomial expressions, and the weighting constant of attributes was determined using a swing weighting method. Because the main objective of this study focuses on the applicability of a multi-attribute utility analysis as a tool for the decision support of countermeasures in early phase of a nuclear accident, less quantifiable attributes were not included due to lack of information. In postulated accidental scenarios for the application of the designed methodology, the variation of the numerical values of total utility for the considered actions, e.g. sheltering, evacuation and no action, was investigated according to the variation of attributes. As a result, it was shown that the numerical values of total utility for the actions are distinctly different depending on the exposure dose and monetary value of dose. As increasing in both attributes, the rank of the numerical values of total utility increased for evacuation, which is more extreme action than for sheltering, while that of no action decreased. As expected probability of high dose is higher, the break-even values for the monetary value of dose, which are the monetary value of dose when the ranking of actions is changed, were lower. In audition, as aversion psychology for dose is higher, the break-even values for dose were lower.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.1067-1080
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• 2006

In empirical evaluations of public projects and public provided goods, MCDA(multicriteria decision-making analysis) has helped decision makers with an adequate policy decision-making tool since it allows taking into account a wide range of assessment criteria. As a tool for decision-making of conflict management, MCDA has demonstrated its usefulness in many public projects such as road, dam and harbor construction. In this study, to use this merit of MCDA, dam project assessment indicators from points of social, economic, environmental and practical views are developed based on sustainable development of water resources, and weighting factors are also estimated by means of questionnaire survey. In order to decide project investment rank, developed evaluation indicators are applied to 6 existing dams under investigation for a rehabilitation project. In addition to, it is recognized that the project practicability has become more important indicator as well as environmental and social issues. This is because cooperation and support from a local government and people are regarded as one of the most important problems in public projects recently.

• Journal of Industrial Convergence
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• v.16 no.4
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• pp.33-46
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• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6605-6608
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• 2015

Background: Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. Materials and Methods: In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Results: Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. Conclusions: A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.281-289
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• 1999

The objective of this paper is to propose a brand new interface mechanism to provide more intelligent decision making support for EC problems. Its main virtue is based on a numerical process mechanism by using an Extended AND-OR Graph (EAOG)-based logic algebra. Using this mechanism, decision makers engaged in electronic commerce (EC) can effectively deal with complicated decision making problems. In the field of traditional expert systems research, AND-OR Graph approach has been suggested as a useful tool for representing the logic flowchart of the forward and/or backward chaining inference methods. However, the AND-OR Graph approach cannot be effectively used in the EC problems in which real-time problem-solving property should be highly required. In this sense, we propose the EAOG inference mechanism for EC problem-solving in which heurisric knowledge necessary for intelligent EC problem-solving can be represented in a form of matrix. Finally, we have proved the validity of our approach with several propositions and an illustrative EC example

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.414-422
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• 2022

Presented in this paper is a methodology for combining a Bayesian statistical approach with Data Quality Objectives (a structured decision-making method) to provide increased levels of confidence in analytical data when approaching a waste boundary. Development of sampling and analysis plans for the characterisation of radioactive waste often use a simple, one pass statistical approach as underpinning for the sampling schedule. Using a Bayesian statistical approach introduces the concept of Prior information giving an adaptive sample strategy based on previous knowledge. This aligns more closely with the iterative approach demanded of the most commonly used structured decision-making tool in this area (Data Quality Objectives) and the potential to provide a more fully underpinned justification than the more traditional statistical approach. The approach described has been developed in a UK regulatory context but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station to demonstrate how the methodology can be applied in this context to support decision making regarding the ultimate disposal option for radioactive waste in a more global context.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1097-1098
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• 2006

This study investigated the needs and effectiveness of the application of Computer-Aided Systems Engineering Tool in order to enhance the chance of success of the large and complex system development projects like City Train System. Furthermore, it was shown that the merits of Systems Engineering and Project Management must be complementary to each other in their interaction in a project. Especially, for the project plans must be executed based on the fully understood system being developed, the technical and management data of the system development need be integrated and be able to support exact and rapid decision making of project planning. As an alternative solution approach, a model for development knowledge capture integrating both technical and management data of system development was proposed using Computer Aided Systems Engineering tool-Cradle.