• Korea Journal of Hospital Management
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• v.10 no.1
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• pp.71-92
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• 2005

The purpose of this study is to analyze the efficiency in management of general hospitals and investigate the major factors on efficiency. Specifically, the management of each general hospital is evaluated by using Data Envelopment Analysis(DEA) technique which is a nonparametric statistical method for measurement of efficiency. Then, the influencing factors are investigated through analyses of Decision-Tree Model and Tobit Regression. The target hospitals were general hospitals in which bed sizes are between 200 and 500 among a total of 276 general hospitals. The main data of financial indicators were collected from 48 hospitals, and it was analyzed by using two statistical models. For Model I, three input and two output variables were used for efficiency evaluation. In particular, three input variables were the number of medical doctors, the number of paramedical personnel, and the bed size. And, two output variables were the numbers of inpatients and outpatients per year, adjusted by bed-size. The results of DEA analysis showed that only seven out of 48 hospitals(15%) turned out to be efficient. The decision-tree analysis also showed that there were six significant influencing factors for Model I. Six factors for Model I were Bed Occupancy Rate, Cost per Adjusted Inpatient, New Visit Ratio of Outpatients, Retired Ratio, Net Profit to Gross Revenues, Net Profit to Total Assets. In addition, the management efficiency of hospital is proved to increase as profit and patient-induced indicators increase and cost-related indicators decrease, by the Tobit regression model of independent variables derived from the decision-tree analysis. This study may be contributable to the development of analytic methodology regarding the efficiency of hospital management in that it suggests the synthetic measures by utilizing DEA model instead of suggesting simple ratio-analyzing results.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.59-68
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• 1998

The goal of data mining is to develop the automatic and intelligent tools and technologies that can find useful knowledge from databases. To meet this goal, we propose an efficient data mining algorithm based on the fuzzy decision tree. The proposed method combines comprehensibility of decision tree such as ID3 and C4.5 and representation power of fuzzy set theory. So, it can generate simple and comprehensive rules describing data. The proposed algorithm consists of two stages: the first stage generates the fuzzy membership functions using histogram analysis, and the second stage constructs a fuzzy decision tree using the fuzzy membership functions. From the testing of the proposed algorithm on the IRIS data and the Wisconsin Breast Cancer data, we found that the proposed method can generate a set of fuzzy rules from data efficiently.

• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• Journal of Korean Society for Quality Management
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• v.42 no.1
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• pp.71-79
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• 2014

Purpose: The purpose of this study is to develop the methods for evaluating patients' queue environment using decision tree and queueing theory. Methods: This study uses CHAID decision tree and M/G/1 queueing theory to estimate pain point and patients waiting time for medical service. This study translates hospital physical data process to logical process to adapt queueing theory. Results: This study indicates that three nodes of the system has predictable problem with patients waiting time and can be improved by relocating patients to other nodes. Conclusion: This study finds out three seek points of the hospital through decision tree analysis and substitution nodes through the queueing theory. Revealing the hospital patients' queue environment, this study has several limitations such as lack of various case and factors.

• IE interfaces
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• v.2 no.1
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• pp.47-58
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• 1989

Many decision problems in the real world have uncertainty and complexity. In many cases, decision makers do not have decision-analytic knowledge enough to solve a given decision problem. This paper developes a Decision Support System(DSS) that can be used for structuring decision problem into decision tree based on the concept of influence diagram and analyzing the decision problem by following Decision Analysis Cycle. This study suggests a DSS system(DACUL) in order to implement Decision Analysis Cycle using Lotus1-2-3. DACUL system has been developed in IBM XT/AT compatible PC.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.525-531
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• 2009

Data mining is the process of extracting hidden patterns from collected data. At this time, for collected data which take important role as the basic information for prediction and recommendation, the process to discriminate incorrect data in order to enhance the performance of analysis result, is needed. The existing methods to discriminate unexpected data from collected data, mainly relies on methods which are based on statistics or simple distance between data. However, for these methods, the problematic point that even meaningful data could be excluded from analysis due that the environment and characteristic of the relevant data are not considered, exists. This study proposes a method to endow human heuristic knowledge with weight value through the comparison between collected data and human heuristic knowledge, and to use the value for creating a decision tree. The data discrimination by the method proposed is more credible as human knowledge is reflected in the created tree. The validity of the proposed method is verified through an experiment.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.455-465
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• 2006

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, and relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these outputs for environmental preservation and environmental improvement.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.395-399
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• 2009

In this work, we describe the syntactic tag prediction system for Korean using the decision tree and CRFs. Generally they select features by their intuition. It depends on their prior knowledge. In this works, we combine features systematically using the decision tree. We also analyze errors and optimize features for the best performance. From the result of experiments, we can see that the proposed method is effective for the syntactic tag estimation and will be helpful for the syntactic analysis.

• ETRI Journal
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• v.32 no.1
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• pp.22-32
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• 2010

The pervasive game environments have activated explosive growth of the Internet over recent decades. Thus, understanding Internet traffic characteristics and precise classification have become important issues in network management, resource provisioning, and game application development. Naturally, much attention has been given to analyzing and modeling game traffic. Little research, however, has been undertaken on the classification of game traffic. In this paper, we perform an interpretive traffic analysis of popular game applications at the transport layer and propose a new classification method based on a simple decision tree, called an alternative decision tree (ADT), which utilizes the statistical traffic characteristics of game applications. Experimental results show that ADT precisely classifies game traffic from other application traffic types with limited traffic features and a small number of packets, while maintaining low complexity by utilizing a simple decision tree.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.652-654
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• 2021

Attacks such as DDoS are detected by the intrusion detection system and can be prevented early. DDoS attack traffic was analyzed using the decision tree. Deterministic features with high importance were found, and the accuracy was verified by proceeding the decision tree for only those properties. And the contents of false positive and false negative traffic were analyzed. As a result, the accuracy of one attribute was 98% and the two attributes were 99.8%, respectively.