• Asian Oncology Nursing
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• v.5 no.1
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• pp.3-10
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• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.6
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• pp.1938-1944
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• 2004

In this study, when we make a diagnosis of constitution using QSCC Ⅱ(Questionnaire of Sasang Constitution Classification). data mining techniques are applied to seek the classification function for improving the accuracy. Data used in the analysis are the questionnaires of 1051 patients who had been treated in Dong Eui Oriental Medical Hospital and Kyung Hee Oriental Medical Hospital. The criteria for data cleansing are the response pattern in the opposite questionnaires and the positive proportion of specific questionnaires in each constitution. And the criteria for variable selection are the test of homogeneity in frequency analysis and the coefficients in the linear discriminant function. Discriminant analysis model and decision tree model are applied to seek the classification function into Sasang constitution. The accuracy in learning sample is similar in two models, the higher accuracy in test sample is obtained in discriminant analysis model.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.25-33
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• 2015

From the viewpoint of applications to manufacturing, data mining is a useful method to find the meaningful knowledge or information about states of processes. But the data from manufacturing processes usually have two characteristics which are multicollinearity and imbalance distribution of data. Two characteristics are main causes which make bias to classification rules and select wrong variables as important variables. In the paper, we propose a new data mining procedure to solve the problem. First, to determine candidate variables, we propose the multiple hypothesis test. Second, to make unbiased classification rules, we propose the decision tree learning method with different weights for each category of quality variable. The experimental result with a real PDP (Plasma display panel) manufacturing data shows that the proposed procedure can make better information than other data mining procedures.

• Korean Journal of Child Studies
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• v.33 no.2
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• pp.111-127
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• 2012

This study compared a number of related variables according to children's stress types using the data mining method. The sample population was taken from the Korean Youth Panel Survey (KYPS) data (2688, sixth-grade elementary students). The results of the decision tree model revealed that : (1) Parental expectations in terms of study, life satisfaction, self-esteem, parental attachment, aggression, the spousal relationship, other cognition (one's own misdeeds), and study related worries were all related to parent stress. (2) Life satisfaction, study related worries, admitting one's own misdeeds, gender, other cognition (one's own misdeeds), aggression, the spousal relationship, and a sense of alienation in the school were all related to appearance stress. (3) Study related worries, parental expectations in terms of study, aggression, life satisfaction, self-esteem, parental attachment, satisfying parental expectations, parental attachment, and teacher attachment were all related to academic stress. (4) A sense of alienation in the school, mixing with peers in the school, aggression, self-esteem, other cognition (one's own misdeeds), study related worries, parental abuse, and life satisfaction were all significantly related to friend stress. These results suggested that children's diverse conditions should be considered according to the stress types if we are to understand and cope with these stress types more efficiently.

• Journal of the Korea Safety Management & Science
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• v.7 no.4
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• pp.61-71
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• 2005

Many researches have been focused on the analysis of industry disasters in order to reduce them. As a similar endeavor, this paper provides a propensity analysis of injured people from various industries using classification and regression tree(CART), a data mining algorithm. The sample for this work was chosen from 25,157data related to various industries during one year ( $2003.2\sim2004.1$ ) at Kangwon-Do in Korea. For the purpose of this paper, eight independent variables (injured date, injured time, injured month, type of Injured person, continuous service period, sex, company size, age)are taken from injured person group. According to the analysis result, it is found that five out of the eight factors that are predicted as significant have salient effects. Factors of season, time/hour, day of the week, or month which disasters happened do not show any significant effect. This paper provides common features of injured people. The provided analysis result will be helpful as a starting point for root cause analysis and reduction of industry disasters and also for development of a guideline of safety management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2564-2588
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• 2024

Due to COVID19 pandemic, most of educational institutions and schools changed the traditional way of teaching to online teaching and learning using well-known Learning Management Systems (LMS) such as Moodle, Canvas, Blackboard, etc. Accordingly, LMS started to generate a large data related to students' characteristics and achievements and other course-related information. This makes it difficult to teachers to monitor students' behaviour and performance. Therefore, a need to support teachers with a tool alerting student who might be in risk based on their recorded activities and achievements in adopted LMS in the school. This paper focuses on the benefits of using recorded data in LMS platforms, specifically Moodle, to predict students' performance by analysing their behavioural data and engagement activities using data mining techniques. As part of the overall process, this study encountered the task of extracting and selecting relevant data features for predicting performance, along with designing the framework and choosing appropriate machine learning techniques. The collected data underwent pre-processing operations to remove random partitions, empty values, duplicates, and code the data. Different machine learning techniques, including k-NN, TREE, Ensembled Tree, SVM, and MLPNNs were applied to the processed data. The results showed that the MLPNNs technique outperformed other classification techniques, achieving a classification accuracy of 93%, while SVM and k-NN achieved 90% and 87% respectively. This indicates the possibility for future research to investigate incorporating other neural network methods for categorizing students using data from LMS.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.257-264
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• 2014

This paper is to construction of the data mining preprocessing tool for efficient database marketing. We compare and evaluate the often used data mining tools based on the access method to local and remote databases, and on the exchange of information resources between different computers. The evaluated preprocessing of data mining tools are Answer Tree, Climentine, Enterprise Miner, Kensington, and Weka. We propose a design principle for an efficient system for data preprocessing for data mining on the distributed networks. This system is based on Java technology including EJB(Enterprise Java Beans) and XML(eXtensible Markup Language).

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.301-315
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• 2010

Recently, competition between companies has intensified and so has the necessity of creating a new business value inventions has increased. A numbers of Business organizations are beginning to realize the importance of business process management. Processes however can often not go the way they were initially designed or non-efficient performance process model could be designed. This can be due to a lack of cooperation and understanding between business analysts and system developers. To solve this problem, business process mining which can be used as the basis of the business process re-engineering has been recognized to an important concept. Current process mining research has only focused their attention on extracting workflow-based process model from competed process logs. Thus there have a limitations in expressing various forms of business processes. The disadvantage in this method is process discovering time and log scanning time in itself take a considerable amount of time. This is due to the re-scanning of the process logs with each new update. In this paper, we will presents a modified FP-Tree algorithm for FP-Tree based business processes, which are used for association analysis in data mining. Our modified algorithm supports the discovery of the appropriate level of process model according to the user's need without re-scanning the entire process logs during updated.