• Journal of Oriental Neuropsychiatry
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• v.16 no.2
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• pp.65-72
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• 2005

Objectives : This study is for discovering the clinical Features of the sleep in ordinary symptoms based on the Sasang Constitution. The result of this study could be helpful to understand and to identify the patients as Soyangin in contrast with the other constitutions -Soeumin, Taeumin and Taeyangin. Methods : There were 1,229 patients(700 female), who answered the questionnaire about their ordinary sleeping patterns. They had been diagnosed, including their clinical Sasang Constitution, by the Sasang Constitution specialist at Bundang Oriental Hospital of Dongguk University. By applying the binary logistic regression analysis, we can measure the characteristics and the influence of ordinary sleeping patterns to the dependent variable(Sasang Constitution). Results : As a result of the binary logistic analysis on the observed questionnaire, we found the characteristics of the ordinary sleeping patterns on Soyangin in contrast with the other constitutions. Firstly, Soyangin has a tendency that he wouldn't dreams more, when he sleeps in contrast with the others. Secondly, Soyangin has a tendency that he wouldn't sleep longer than 6-7hours. Thirthly, Soyangin has a tendency that he will sleep well in contrast with the others. Conclusion : This study will be used to identify patients as Soyangin in contrast with the others-Soemin, Taeumin and Taeyangin.

• Journal of Sasang Constitutional Medicine
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• v.16 no.3
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• pp.18-24
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• 2004

1. Objectives This study is for discovering the characteristics of the sleep in ordinary symptoms based on the Sasang Constitution. The result of this study could be helpful to understand and to identify the patients as Taeumin by contrast with non-Taeumin. 2. Methods There were 1,229 patients(529 men), who answered the questionnaire about their ordinary sleep patterns. They had been diagnosed, including their Sasang Constitution, by the Sasang Constitution specialist at Bundang Oriental Hospital of Dongguk university. Applying the binary logistic regression analysis to each item about ordinary sleep patterns and the diagnosed Sasang Constitution, we can measure the characteristics and influence of ordinary sleep patterns to the dependent variable(Sasang Constitution). 3. Results As a result of the binary logistic analysis on the observed questionnaire, we found the similar features of the ordinary sleep patterns on Taeumin by contrast with non-Taeumin. In order of influence that identify Taeumin or non-Taeumin, first, Taeumin has a tendency that he will snore well or frequently. Second, Taeumin has a tendency that he will not dream, when he sleeps by contrast with non-Taeumin. 4. Conclusions This study will be used to identify patients as Taeumin or non-Taeumin by means of a reference tool.

• 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.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.366-374
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• 2015

This study investigated what factors would affect suicidal ideation of the people who have received public assistance. For the purpose, the survey results of the 7th year(2012) of Korea Welfare Panel Study, which were conducted by Korea Institute for health and Social Affairs and Social Welfare Research Institute of Seoul National University, were used for analysis. In order to figure out the level of influence on the suicidal ideation, a binary logistic regression analysis using a binary logistic model was used as an analysis method. As a result, it was found that when the subjects are middle school graduates, and if they are married, there are low suicidal ideation. Also, the higher their self-esteem is and the higher their satisfaction with public assistance, the lower there they have suicidal ideation. Furthermore, it was proved that if they have depression, or in middle age, they have high possibility of suicidal ideation. It was proved that satisfaction with public assistance also can have influence on the suicidal ideation of the poor class, not only physical and psychological factors. Therefore, measuring the satisfaction of the recipients with public assistance can be one of the significant factors that affects suicidal ideation.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.1-11
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• 2012

We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.4
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• pp.13-30
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• 2020

This study aims to analyze the qualitative and quantitative effects of environmental policies in terms of green space management using logistic regression model(LRM). Landsat satellite imageries in 1985, 1992, 2000, 2008, and 2015 are classified using a hybrid-classification method. Based on these classified maps, logistic regression model having a deforestation tendency of the past is built. Binary green space change map is used for the dependent variable and four explanatory variables are used: distance from green space, distance from settlements, elevation, and slope. The green space map of 2008 and 2015 is predicted using the constructed model. The conservation effect of Ulsan's environmental policies is quantified through the numerical comparison of green area between the predicted and real data. Time-series analysis of green space showed that restoration and destruction of green space are highly related to human activities rather than natural land transition. The effect of green space management policy was spatially-explicit and brought a significant increase in green space. Furthermore, as a result of quantitative analysis, Ulsan's environmental policy had effects of conserving and restoring 111.75㎢ and 175.45㎢ respectively for the periods of eight and fifteen years. Among four variables, slope was the most determinant factor that accounts for the destruction of green space in the city. This study presents logistic regression model as a way of evaluating the effect of environmental policies that have been practiced in the city. It has its significance in that it allows us a comprehensive understanding of the effect by considering every direct and indirect effect from other domains, such as air and water, on green space. We conclude discussing practicability of implementing environmental policy in terms of green space management with the focus on a non-statutory plan.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.56-62
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• 2021

High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.

• Journal of the Korean Society of Physical Medicine
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• v.11 no.3
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• pp.73-80
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• 2016

PURPOSE: The purpose of this study was to investigate the quality of life related to fall experiences in chronic stroke patients. METHODS: This cross-sectional study included 117 patients with stroke from 3 hospitals in D metropolitan city. General characteristics, including fall experiences and quality of life, were assessed through a face-to-face interviews conducted in a quiet place using a questionnaire. Measurement of quality of life in stroke patients was conducted using the Korean Stroke Specific Quality of Life Scale (SS-QOL). To identify the SS-QOL items related to fall experiences, the items of the SS-QOL were considered as independent variables, and the variables that were significantly different according to fall experiences were identified using a univariate analysis. A binary logistic regression was then performed using fall experiences as the independent variable. RESULTS: According to the univariate analysis, self help activities, social role, and upper extremity function were significantly lower in the fall group than that in the non-fall group (p<.05). The findings of the binary logistic regression confirmed that social roles and upper extremity function were the SS-QOL items that were related to fall experience in chronic stroke patients. CONCLUSION: These findings suggest that social roles and upper extremity function may be risk factors for fall experience in patients with chronic stroke.