• Journal of Hospice and Palliative Care
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• v.25 no.2
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• pp.76-84
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• 2022

Purpose: We compared cost-effectiveness parameters between inpatient and home-based hospice-palliative care services for terminal cancer patients in Korea. Methods: A decision-analytic Markov model was used to compare the cost-effectiveness of hospice-palliative care in an inpatient unit (inpatient-start group) and at home (home-start group). The model adopted a healthcare system perspective, with a 9-week horizon and a 1-week cycle length. The transition probabilities were calculated based on the reports from the Korean National Cancer Center in 2017 and Health Insurance Review & Assessment Service in 2020. Quality of life (QOL) was converted to the quality-adjusted life week (QALW). Modeling and cost-effectiveness analysis were performed with TreeAge software. The weekly medical cost was estimated to be 2,481,479 Korean won (KRW) for inpatient hospice-palliative care and 225,688 KRW for home-based hospice-palliative care. One-way sensitivity analysis was used to assess the impact of different scenarios and assumptions on the model results. Results: Compared with the inpatient-start group, the incremental cost of the home-start group was 697,657 KRW, and the incremental effectiveness based on QOL was 0.88 QALW. The incremental cost-effectiveness ratio (ICER) of the home-start group was 796,476 KRW/QALW. Based on one-way sensitivity analyses, the ICER was predicted to increase to 1,626,988 KRW/QALW if the weekly cost of home-based hospice doubled, but it was estimated to decrease to -2,898,361 KRW/QALW if death rates at home doubled. Conclusion: Home-based hospice-palliative care may be more cost-effective than inpatient hospice-palliative care. Home-based hospice appears to be affordable even if the associated medical expenditures double.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.403-412
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• 2013

This study figures out the concepts of container transport, logistical cost and the distribution of a company through studying documents, and to suggest logistical cost reduction approach, focused on the efficiency of transport which occupied the considerable portion of the total logistical cost of the company. We analyze and discuss the container loading of multiple items for multiple places of departure and arrival through a case study on S company in South Korea. We suggest a direction to reduce the logistical cost of the companies, analyzing the conditions of multiple items loading, and rule-based systems including an algorithm which determines container-loading for minimum freight expenses. We use data mining and OLAP tools of MS Analysis Services to produce loading rules for multiple items loading and generate OLAP cube and decision trees to validate the rules.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.128-132
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• 2001

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.2
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• pp.101-109
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• 1998

Recently, improvement of the quality of urban riverscape environment has been emphasized not only by landscape architectural field but also by various professionals in planning and ecology. Regarding to this current movement, the aim of this paper is to highlight major visual elements of riverscape axis as a case study of Shinchon River in Taegu City to suggest some basic guidelines for arranging riverscape in urban area. The study was mainly based on Repertory Grid Development method which was developed in Japan. The method is consist of three steps such as decision of element landscape in study area for slide photos, selection of evaluation items for interview and obstraction of proper evaluation factors. The major findings through this study are as follows; 1) The 12 major visual elements which possibly improve riverscape, based on abstraction of proper evaluation factors, are Dunchi, surface of the water,, equipment of river, buildings near riverside, river vertical and horizontal facilities like bridge, fine view, riverbed, water plant, naturalness, water's edge line, harmony and street trees by order. 2) Total numbers of adjective which describe 12 common factors are 25, such as clean, open, stable, quiet, comfortable, friendly, bright, natural etc. In addition, Dunchi was described 337 times by various adjectives, surface of the water was 200 times and arrangement of river was 146 times which is similar result with the order of 12 influential common factors. 3) Therefore, Dunchi, surface of the water and equipment of river are three most important factors which could create better riverscape. These three factors implies us how we supply good quality of urban river environment for the urban residents.

• Bulletin of the Korean Chemical Society
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• v.29 no.1
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• pp.111-116
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• 2008

A piperazinylalkylisoxazole library containing 86 compounds was constructed and evaluated for the binding affinities to dopamine (D3) and serotonin (5-HT2A/2C) receptor to develop antipsychotics. Dopamine antagonists (DA) showing selectivity for D3 receptor over the D2 receptor, serotonin antagonists (SA), and serotonin-dopamine dual antagonists (SDA) were identified based on their binding affinity and selectivity. The analogues were divided into three groups of 7 DAs (D3), 33 SAs (5-HT2A/2C), and 46 SDAs (D3 and 5-HT2A/2C). A classification model was generated for identifying structural characteristics of those antagonists with different affinity profiles. On the basis of the results from our previous study, we conducted the generation of the decision trees by the recursive-partitioning (RP) method using Cerius2 2D descriptors, and identified and interpreted the descriptors that discriminate in-house antipsychotic compounds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.63-69
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• 2010

Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. This paper performs an empirical comparison of Boosted neural networks and traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.

• Genomics & Informatics
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• v.1 no.2
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• pp.65-74
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• 2003

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

• Transactions of the Korean hydrogen and new energy society
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• v.19 no.6
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• pp.552-560
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• 2008

In 1997, Korean government established the National Energy and Resources Plan, which targeted from 1997 to 2005 with strategic energy technology development. At the end of 2005, Korean government built a New National Energy and Resources Plan preparing for upcoming 10 years from 2006 until 2015 based on energy technology trees comparing with the previous plan, which based on the energy R&D projects. In this research, we prioritize the relative preferences and efficiency by an AHP/DEA CCR-I and BCC-I integrated model approach considering scale efficiency for well focused R&D and efficiency of developing Greenhouse Gas technologies as an extended research from a view point of econometrics as an extended research.

• Asia pacific journal of information systems
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• v.12 no.1
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• pp.139-158
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• 2002

As the telecommunication market becomes mature in Korea, severe competition has already begun on the market. While service providers struggled for the last couple of years to acquire as many new customers as possible, nowadays they are making more efforts on retaining the current customers. The churn management by analyzing customers' demographic and transactional data becomes one of the key customer retention strategies which most companies pursue. However, the customer data analysis has still remained at the basic level in the industry, even though it has considerable potential as a tool for understanding customer behavior. This paper develops several churn prediction models using data mining techniques such as logistic regression, decision trees, and neural networks. For model-building, real data were used which were collected from one of the major telecommunication companies in Korea. This paper explores various ways of comparing model performance, while the hit ratio was mainly focused in the previous research. The comparison criteria used in this study include gain ratio, Kolmogorov-Smirnov statistics, distribution of the predicted values, and explanation ability. This paper also suggest some guidance for model selection in applying data mining techniques.