• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3400-3411
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• 2013

The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. This study was analyzed by decision making tree model using Clementine C&RT(Classification & Regression Tree, CART) as data mining technique. We utilized this data mining technique to analyze medical record of 1,500 people. Whole data were assorted by length of stay in PACU and divided into 3 groups. The result extracted by C5.0 decision tree method showed that important related factors for lengh of stay in PACU are type of operation, preoperative EKG abnormality, anesthetics, operative duration, age.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.193-206
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• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.299-306
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• 2020

The purpose of this study is to find out specific measures that can help the management strategy of patient-centered medical institutions by conducting research on patient experience surveys of convergence outpatient medical services using data mining techniques according to changes in patient-centered medical culture. Using the raw data of the 2018 Medical Service Experience Survey, 8,843 people over the age of 15 who had patient experience in outpatient medical services were analyzed. Decision tree analysis was performed. The determinants of satisfaction with outpatient medical services patient experience were the doctor's area and patient's rights protection area, and the determinants of intention to recommend outpatient medical services were the doctor's area and facilities comfort. Women evaluated the experience positively in overall satisfaction as compared to men, and those over the age of 60 positively evaluated the overall satisfaction and intention to recommend. It is significant that the outpatient experience decision-making model is presented, and that the doctor's area, patient's rights protection area, and facility comfort are important factors. Long-term research on the 'Medical Service Experience Survey' is needed, and research on the inpatient medical service experience is needed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.269-275
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• 2004

Geographic data have widely been applied in different areas including landuse, city planning and management, environment, disaster management and even daily use of citizens. Since geographic data have been built individually using different methods, many problems such as data inconsistency, duplicated investment, and confusion in decision making have arisen. Thus, the necessity of national framework database that can be shared by different areas has increased. As a result, eight fields of the framework database were defined by NGIS Law and 19 detailed items were selected. This study used the AHP (Analytical Hierarchy Process) and the decision tree to evaluate the relative importance of the items (eg. roads, railroads, coastline, surveying control points, and etc.) and presented the groups classified according to the priorities of the items. The result of this study is believed to contribute to effective budget planning for building national framework database.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.9-21
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• 2023

The recent conflict between Russia and Ukraine underscores the significant of military logistics support in modern warfare. Military logistics support is intricate and specialized, and traditionally centered on the mission-level operational analysis and functional models. Nevertheless, there is currently increasing demand for military logistics support even at the engagement level, especially for resupply using unmanned transport assets. In response to the demand, this study proposes a task model of the military logistics support for engagement-level analysis that relies on the logic of ammunition resupply below the battalion level. The model employs a decisions tree to establish the priority of resupply based on variables such as the enemy's level of threat and the remaining ammunition of the supported unit. The model's feasibility is demonstrated through a combat simulation using OneSAF.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.325-330
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• 2023

This paper proposes to apply machine learning to the process of predicting the slab thickness based on the structural analysis results or experience and subjectivity of engineers in the design of bridge data construction to enable digital-based decision-making. This study aims to build a reliable design environment by utilizing machine learning techniques to provide guide values to engineers in addition to structural analysis for slab thickness selection. Based on girder bridges, which account for the largest proportion of bridge data, a prediction model process for predicting slab thickness among superstructures was defined. Various machine learning models (Linear Regress, Decision Tree, Random Forest, and Muliti-layer Perceptron) were competed for each process to produce the prediction value for each process, and the optimal model was derived. Through this study, the applicability of machine learning techniques was confirmed in areas where slab thickness was predicted only through existing structural analysis, and an accuracy of 95.4% was also obtained. models can be utilized in a more reliable construction environment if the accuracy of the prediction model is improved by expanding the process

• Spatial Information Research
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• v.20 no.6
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• pp.77-90
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• 2012

In this paper, we examined how to utilize QR codes for meeting the information demand and making better use of the paper map. By Decision Tree Analysis, we investigated whether to have any intention to use the paper map with QR codes for receiving more information and what decision variables affect the answers. Thus, we also surveyed the area of providing information and sectoral demand for deriving additional information demand to being provided through QR codes. In the results of our study, we confirmed that the decision variables, to make any intention to use the paper map with QR code, are the frequency of using the paper, the experience of using the paper map, the intention to buy the paper map, the experience of using QR codes and the experience of buying the paper map. In these variables, the frequency of using the paper map is a major factor to decide whether it is intended to use the paper map with QR codes. we also identified that there are various additional information demand using the paper map with QR codes in the area of 'Daily life', 'Real estate', 'Education', 'Travel and Leisure', and 'Entertainment'. Especially additional information demand is high in the area of 'Travel and Leisure'. These results could be used to find a way how to vitalize the usage of paper map by introduction of QR codes and how to develop QR codes for the paper map and concerning applications.

• Knowledge Management Research
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• v.21 no.3
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• pp.105-121
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• 2020

With regard to the development of new drugs, what is most important for a Korean Biotech, where no global sales network has been established, is decision-making related to out-licensing of new drugs. The probability of success for each clinical phase is different, and the licensing amount and its royalty vary depending on which clinical phase the licensing contract is made. Due to the nature of such a licensing contract and Biotech's weak financial status, it is a very important decision-making issue for a Biotech to determine when to license out to a Big Pharma. This study defined a model called 'optimal timing for out-licensing of new drugs' and the results were derived from the decision tree analysis. As a case study, we applied to a Biotech in Korea, which is conducting FDA global clinical trials for a first-in-class new drug. Assuming that the market size and expected market penetration rate of the target disease are known, it has been shown that out-licensing after phase 1 or phase 2 of clinical trials is a best alternative that maximizes Biotech's profits. This study can provide a conceptual framework for the use of management science methodologies in pharmaceutical fields, thus laying the foundation for knowledge and research on out-licensing of new drugs.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.