• Land and Housing Review
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• v.14 no.1
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• pp.67-82
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• 2023

Using the Artificial Neural Network model and Binary Logistic Regression model, this study investigates influence factors on the quality of life in terms of housing environment during the COVID-19 in Seoul. The results show that the lower the satisfaction level of housing policy, the lower the quality of life in the employment field and the lower the quality of residential field. On the other hand, permanent workers and self-employed respondents have experienced improvement in residential quality during the pandemic. A limitation of this study is associated with disentangling the causal relationship using the 'black box' characteristics of ANN method.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.297-307
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• 2009

Korean household debt is one of the main sources of the current financial crisis. This paper studies the impact of household members' attributes such as a type of housing(self-own or rent), education, age, average monthly income of the head of household, and the area of residence, on the stress level of the household members due to household debt. We analyze a real data set collected by KB Kookmin Bank in 2004. We consider low and high stress level as a binary response variable and use a logistic regression model with the attributes of household members as explanatory variables. A simple but well-fitting model is selected by backward elimination method based on the likelihood statistic for goodness-of-fit test, and the impact of the attributes on the stress level is studied from parameter estimates of the selected model. We also perform the similar analysis on a binary response variable which distinguishes households with no debt from the rest. From the analysis, the stress level tends to be low for households with self-own houses, high average monthly income, low education level, and young members.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.1-10
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• 2019

Research classification of software modules was done to validate the approaches proposed for addressing limitations in existing classification approaches. The objective of this study was to replicate the experiments of a recently published research study and re-evaluate its results. The reason to repeat the experiment(s) and re-evaluate the results was to verify the approach to identify the faulty and non-faulty modules applied in the original study for the prioritization of test cases. As a methodology, we conducted this study to re-evaluate the results of the study. The results showed that binary logistic regression analysis remains helpful for researchers for predictions, as it provides an overall prediction of accuracy in percentage. Our study shows a prediction accuracy of 92.9% for the PureMVC Java open source program, while the original study showed an 82% prediction accuracy for the same Java program classes. It is believed by the authors that future research can refine the criteria used to classify classes of web systems written in various programming languages based on the results of this study.

• 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 Sasang Constitutional Medicine
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• v.17 no.3
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• pp.82-90
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• 2005

1. Objectives : There are many studies on the ordinary symptoms based on Sasang constitution. But there are not quite satisfactory between the types of Sasang constitution. So This study is for learning the characteristics of ordinary symptoms depending on Taeyangin Soyangin(the rest Yangin) and Taeumin Soeumin(the rest Eumin) of the Sasang constitution. 2. Methods : We classified them into the each type of Sasang Constitutional Medicine(SCM) by the well-trained SCM specialist, and assessed their ordinary features by the questionnaire. Binary logistic regression analysis was applied to evaluate the influence of ordinary features to the diagnosis of SCM. 3. Results : There are a result of the binary logistic analysis on the observed questionnaire. 1) Regarding sleeping, Yangin do not dream much as Eumin do when they sleep. The time they are sleeping is not longer, and they usually do not sleep well. 2) Regarding stools, Yangin go to stool more than Eumin do. The constipation does not occur when they are not in a good condition, and they do not feel uncomfortable when they do not go to stool for a day. The length of time taken for emptying the bowels is much longer, and the hardness of their stools is much more, but the hardness does not mean that they have the constipation. 3) Regarding urine, Yangin have much more foam than Eumin. 4. Conclusions: We found that Yangin and Eumin have characteristics of ordinary symptoms, but partly there are not in accordance with ones what Lee Je-ma said in his book. So in future we hope clinical studies are required steadily.

• The Journal of Korean Obstetrics and Gynecology
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• v.23 no.3
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• pp.173-183
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• 2010

Purpose: To estimate the effect of oriental treatment on recurrent pregnancy loss, a retrospective analysis was done. Methods: Sixty-eight pregnant women at the $\bigcirc\bigcirc$ oriental clinic, Korea, from January 2005 to May 2009 diagnosed as recurrent pregnancy loss were included in this study. The sixty-eight patients received oriental treatment such as acupuncture, moxibution, herbal acupuncture and herbal prescriptions, divided into two groups: Group A- live birth(N=58) and Group B- abortion(N=10). The maternal age, parity, menstrual history, gynecological history and period of treatment were compared. To find out factors affecting the success of birth, we performed binary logistic regression analysis(SPSS ver. 14.0 for windows). Results: The live birth rate was 85.3%. The maternal age, parity, menstrual history, gynecological history and period of treatment were not different between two groups. Logistic regression analysis showed that the significant factors predicting the occurrence of miscarriage were advanced maternal age(${\geq}35$)(P=0.005, Odds Ratio[OR]=3.809, 95% Confidence Interval[95%CI]: 1.514-9.585) and suffering from gynecological problems(P=0.044, OR=4.048, 95%CI: 1.037-15.801). Conclusions: The results suggest that oriental treatment has effectiveness on recurrent pregnancy loss. Further study will be needed.

• Journal of Korean society of Dental Hygiene
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• v.19 no.1
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• pp.117-129
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• 2019

Objectives: The purpose of this study was to investigate the influence of demographic characteristics and oral health status on unmet dental needs among preschool children and to provide a basis for improvement of the dental care equality and accessibility using data from the $6^{th}$ National Health and Nutrition Survey. Methods: This study was performed using data collected from the $6^{th}$ National Health and Nutrition Survey. The subjects were 1,472 out of 22,940 people, who participated in the survey and under went oral examination. IBM SPSS Statistics (Version 20.0) was used for statistical analyses based on the complex sampling design. Frequency analysis was performed to determine the distribution of unmet dental needs according to the characteristics of the subjects. The Rao-Scott ${\chi}^2$ test was performed to examine the relationship of unmet dental needs with general characteristics and health- and oral health-related variables. Relevant factors were determined using binary logistic regression analysis. Results: The factors that had statistically significant relations with unmet dental needs included age, medical insurance, household income, limited physical activity, history of dental caries in deciduous teeth, and subjective health status. Logistic regression analysis of complex samples was conducted to determine factors related to unmet dental needs. The results of analysis showed that limited physical activity and history of dental caries in deciduous teeth were related to unmet dental needs. Conclusions: The results show the factors affecting, and the reasons for, the unmet dental needs of preschool children. Future studies are needed to develop national projects and oral health education reforms to address inequalities in preschool children's dental care.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• Korean Management Science Review
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• v.34 no.1
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• pp.27-45
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• 2017

The purpose of this study is to empirically analyze whether open innovation activities are significant and which methods are more effective in developing new businesses. Based on the latest technological innovation survey data of the Science and Technology Policy Institute, the results were analyzed by binary logistic regression analysis. As a result of the analysis, we confirmed that open innovation activities have a positive effect on the performance of developing new businesses. In the open innovation activities, Recruitment (invitation) of specialist in related fields, Business alliance technical agreement, Dispatch of personnel, M&A, Acquisitions identify related field trends showed more influence in order. It would be beneficial to improve the performance of developing new businesses with a low probability of success if utilize more effective innovation activities in developing new business in enterprises or organizations throughout this study.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2847-2850
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• 2014

Background: Retinoblastoma is a rare malignant intraocular neoplasm. About 90% of cases feature a germline mutation in the RB1 gene and these will develop retinoblastoma during their early childhood. An association between mutations in germline cells and aging has been demonstrated. This suggests a higher incidence of childhood cancer including retinoblastoma among children of older parents. Materials and Methods: In the present study we aimed to determine the association of paternal and maternal age with an increased risk of retinoblastoma in a case-control study in Iranian population. The study was carried out on 240 persons who were born during 1984-2012 in Mahak and Mofid hospitals in Tehran, Iran. The statistical analysis included studying the mean age of parents and in order to know whether parental age of patients is different from parental age of control group, (t-test) compare averages test is used perfectly. By binary logistic regression, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Results: The results of statistical analysis including the study of mean parental age by the use of (t-test) compare averages test showed a significant difference between parental ages of patients and controls. Logistic regression showed that coefficients were significant for maternal but not paternal age. Conclusions: Our findings indicate that advanced maternal age can increase the risk of retinoblastoma in offspring, but the paternal age has no significant effect.