• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.197-208
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• 2023

In this study, we attempted to examine the changing ways of thinking about lecture evaluation before and after COVID-19. To this end, decision tree analysis(Decision Tree) was used among data mining techniques based on lecture evaluation data for liberal arts and major classes conducted before and after COVID-19 for A university. According to the results of the study, liberal arts changed from 'method' to 'content', and 'knowledge improvement' was an important factor both before and after majors. In particular, 'Assignment' was found to be an important factor after the COVID-19 in common in the evaluation of lectures in the liberal arts department, which means that in the future, professors will be provided with appropriate teaching methods during class, interaction with students, and feedback on assignments or test results, indicates the need for competence. Based on the results of this study, a plan to improve communication with students and activation of blended learning was suggested.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.28-37
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• 2011

With growing concerns about healthy life recently, although the podiatry which deals with the whole area for diagnosis, treatment of foot and leg, and prevention has been widely interested, research in our country is not active. Also, because most of the previous researches in data analysis performed the quantitative approaches, the reasonable level of reliability for clinical application could not be guaranteed. Clinical data mining utilizes various data mining analysis methods for clinical data, which provides decision support for expert's diagnosis and treatment for the patients. Because the decision tree can provide good explanation and description for the analysis procedure and is easy to interpret the results, it is simple to apply for clinical problems. This study investigate rules of item of diagnosis in disease types for adapting decision tree after collecting diagnosed data patients who are 2620 feet of 1310(males:633, females:677) in shoes clinic (department of rehabilitation medicine, Chungnam National University Hospital). and we classified 15 foot diseases followed factor of 22 foot diseases, which investigated diagnosis of 64 rules. Also, we analyzed and compared correlation relationship of characteristic of disease and factor in types through made decision tree from 5 class types(infants, child, adolescent, adult, total). Investigated results can be used qualitative and useful knowledge for clinical expert`s, also can be used tool for taking effective and accurate diagnosis.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.297-309
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• 2020

This study focuses on the application of the data mining technique logistic regression analysis and decision tree analysis to the domestic public database called Korean Children Youth Panel Survey (KCYPS) to derive a series of important factors affecting the enhancement of life satisfaction of domestic youth. As a result, the general impact factors on life satisfaction for each grade were derived from logistic regression. Using decision tree analysis, we came to conclusions that those factors such as depression, overall grade satisfaction, household economic level, and school adaptation play crucial roles in affecting high school adolesscents' life satisfaction.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.1-7
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• 2021

The number of new students at many domestic universities is declining. In particular, private universities, which are highly dependent on tuition, are experiencing a crisis of existence. Amid the declining school-age population, universities are striving to fill new students by improving the quality of education and increasing the student employment rate. Recently, there is an increasing number of cases of using the accumulated big data of universities to prepare measures to fill new students. A representative example of this is the analysis of factors that affect student employment. Existing employment-influencing factor analysis studies have applied quantitative models such as regression analysis to university big data. However, since the factors affecting employment differ by major, it is necessary to reflect this. In this paper, the factors affecting employment by major are analyzed using the data of University C and the decision tree model. In addition, based on the analysis results, a roadmap for student employment by major is recommended. As a result of the experiment, four decision tree models were constructed for each major, and factors affecting employment by major and roadmap were derived.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.1-11
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• 2023

Clustering analysis is used in various fields including customer segmentation and clustering methods such as k-means are actively applied in the credit card customer segmentation. In this paper, we summarized the input features selection method of k-means clustering for the case of the credit card customer segmentation problem, and evaluated its feasibility through the analysis results. By using the label values of k-means clustering results as target features of a decision tree classification, we composed a method for prioritizing input features using the information gain of the branch. It is not easy to determine effectiveness with the clustering effectiveness index, but in the case of the CH index, cluster effectiveness is improved evidently in the method presented in this paper compared to the case of randomly determining priorities. The suggested method can be used for effectiveness of actively used clustering analysis including k-means method.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.751-758
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• 2021

Objective: Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis. Materials and Methods: In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error. Results: Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65-95%), 100% (41 out of 41; 95% CI, 91-100%), 95% (59 out of 61; 95% CI, 87-98%), and 0.966 (95% CI, 0.912-1.000), respectively. Conclusion: Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.

• Journal of Hospice and Palliative Care
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• v.24 no.3
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• pp.184-193
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• 2021

Purpose: This study attempted to develop clinical guidelines to help patients use hospice and palliative care (HPC) at an appropriate time after writing physician orders for life-sustaining treatment (POLST) by identifying the characteristics of HPC use of patients with terminal cancer. Methods: This retrospective study was conducted to understand the characteristics of HPC use of patients with terminal cancer through decision tree analysis. The participants were 394 terminal cancer patients who were hospitalized at a cancer-specialized hospital in Seoul, South Korea and wrote POLST from January 1, 2019 to March 31, 2021. Results: The predictive model for the characteristics of HPC use showed three main nodes (living together, pain control, and period to death after writing POLST). The decision tree analysis of HPC use by terminal cancer patients showed that the most likely group to use HPC use was terminal cancer patients who had a cohabitant, received pain control, and died 2 months or more after writing a POLST. The probability of HPC usage rate in this group was 87.5%. The next most likely group to use HPC had a cohabitant and received pain control; 64.8% of this group used HPC. Finally, 55.1% of participants who had a cohabitant used HPC, which was a significantly higher proportion than that of participants who did not have a cohabitant (1.7%). Conclusion: This study provides meaningful clinical evidence to help make decisions on HPC use more easily at an appropriate time.

• Journal of Korean Society on Water Environment
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• v.32 no.3
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• pp.253-260
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• 2016

To reduce energy consumption in wastewater treatment plants (WWTPs), renewable energy applications such as small hydropower, solar energy, and wind energy are popular. However, it should be noticed that energy originated from operation of wastewater treatment process can be reduced through optimized operation based on analysis of factors affecting energy. In this research, the relationship to the various operational variables and influent factors was explored using correlation analysis and decision tree algorithm. Due to the non-linear characteristics of the process, it was difficult to find clear linear patterns through correlation analysis. However, decision tree algorithm showed its usefulness in uncovering hidden patterns that consume energy. As operational factors, influent flowrate, the amount of aeration, nitrate recycling pumping rate, and sludge wasting pumping rate were selected as important factors. For environmental factors associated with influent compositions and removal rate, BOD and T-N removal rate were selected as significant factors.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.179-189
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• 2018

Currently, research on smart factories is steadily being carried out in terms of implementation strategies and considerations in construction. Various studies have not been conducted on companies that introduced smart factories. This study conducted a questionnaire survey for SMEs applying the basic stage of smart factory. And the cluster analysis was conducted to examine the characteristics of the company. In addition, we conducted Decision Tree and Naive Bay to examine how the characteristics of a company are derived and compare the results. As a result of the cluster analysis, it was confirmed that the group was divided into the high satisfaction group and the low satisfaction group. The decision tree and the Naive Bay analysis showed that the higher satisfaction group has high productivity.

• Journal of Engineering Education Research
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• v.21 no.4
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• pp.46-52
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• 2018

In general, lecture evaluation has been used in most universities as an important criterion to evaluate quality of education. This study is exploratory research on the predictors that determine lecture evaluation in college of engineering to give practical implications for improvement of engineering education. For the exploration of predictors of lecture evaluation, the data of lecture evaluation in A College of Engineering located in the metropolitan area was used, and Decision Tree Analysis was utilized as an analysis method. As a result, the characteristics of students turned out to be the most distinct predictor comparing with those of course and instructor at lecture evaluation in college of engineering. That is, as various elements other than teaching competency influence lecture evaluation in college of engineering, it is necessary to be more careful in evaluating quality of lecture or teaching competence. Thus, a follow-up study should be conducted to adjust the influence by the predictors that instructors can hardly control.