• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.51-60
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• 2018

This study aims to understand crucial factors affecting user's Fintech payment service adoption. On the basis of innovation diffusion theory and prior Fintech literature, this study classifies the influence factors of users' adoption of Fintech payment service into two dimensions - service dimension containing complexity, perceived benefit, trust in service provider and user dimension containing personal innovativeness and security breach experience. The data analysis results using binary logistic regression shows the negative direct effects of perceived risk, complexity, security accident experience on user's service adoption are statistically significant. Personal innovativeness has a positive effect on user's Fintech payment service adoption. The moderation effect of security accident experience is also significant at p<0.05.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.1-13
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• 2020

The empty houses' problem is important in the local revitalization and local sustainability, and these phenomenon caused by various factors of the region. The population and housing census data are the most effective data available to study this phenomenon by small regions. In this study, logistic regression and multiple regression analysis were performed to understand the effects of population, household, and housing characteristics on empty houses using population and housing census data. Also, the scale and direction of the effect of each characteristic in large cities, small cities, and rural areas were compared. As results, there was a slight difference between cities and province regions in the district and housing characteristic variables. In the comparison of Eup-Myeon-Dong, the affected variables were different in the Dong and Myeon areas. The significance of this study is to examine the effect of the characteristics of population and housing on the vacant houses and to confirm that the factors affecting different regions.

• 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.

• Imaging Science in Dentistry
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• v.48 no.4
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• pp.277-281
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• 2018

Purpose: This study was performed to investigate the relationship between sleep bruxism(SB) and pulpal calcifications in young women. Materials and Methods: A total of 100 female participants between 20 and 31 years of age who were referred to our radiology clinic for a dental check-up, including 59 SB and 41 non-SB patients, were sampled for the analysis. SB was diagnosed based on the American Academy of Sleep Medicine criteria. All teeth were evaluated on digital panoramic radiographs to detect pulpal calcifications, except third molars, teeth with root canal treatment, and teeth with root resorption. Binary logistic regression analysis was used to determine the risk factors for pulpal calcifications. The Spearman correlation coefficient was applied and the Pearson chi-square test was used for categorical variables. To test intra-examiner reproducibility, Cohen kappa analysis was applied. P values <.05 were considered to indicate statistical significance. Results: A total of 2800 teeth were evaluated (1652 teeth from SB patients and 1148 from non-SB patients), and 61% of patients had at least 1 dental pulpal calcification. No statistically significant relationship was found between SB and pulpal calcifications (P>0.05). In SB patients, the total number of pulpal calcifications was 129, while in non-SB patients, it was 84. Binary logistic analysis showed that SB was not a risk factor for the presence of pulpal calcifications(odds ratio, 1.19; 95% CI, 0.52-2.69, P>.05). Conclusion: No relationship was found between SB and pulpal calcifications.

• Journal of Korean Society of Rural Planning
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• v.16 no.2
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• pp.47-55
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• 2010

Land cover and land use change data are important in many studies including climate change and hydrological studies. Although the various theories and models have been developed, it is difficult to identify the driving factors of the land use change because land use change is related to policy options and natural and socio-economic conditions. This study is to attempt to simulate the land cover change using the CLUE model based on a statistical analysis of land-use change. CLUE model has dynamic modeling tools from the competition among land use change in between driving force and land use, so that this model depends on statistical relations between land use change and driving factors. In this study, Yongin, Icheon and Anseong were selected for the study areas, and binary logistic regression and factor analysis were performed verifying with ROC curve. Land cover probability map was also prepared to compare with the land cover data and higher probability areas are well matched with the present land cover demonstrating CLUE model applicability.

• Journal of Oriental Neuropsychiatry
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• v.16 no.1
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• pp.185-192
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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 Soeumin in contrast with the other constitutions-Soyangin, 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 Soeumin in contrast the other constitutions. Firstly, Soeumin has a tendency that he wouldn't snore well in comparison with the others. Secondly, Soeumin has a tendency that he will dreams more, when he sleeps in contrast with the others. Thirthly, Soeumin has a tendency that he will sleep longer than 6-7hours. Fourthly, Soeumin has a tendency that he will struggle during sleeping in contrast with the others. Conclustion : This study will be used to identify patients as Soeumin in contrast with the others-Soyangin, Taeumin and Taeyangin.

• Journal of Environmental Impact Assessment
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• v.22 no.3
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• pp.231-240
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• 2013

The Purpose of this research was to propose positioning strategies of urban park (UP) based on the assessment of location characteristics at cheongju city. To do that, this research found out urban park service area (UPSA) using GIS network analysis and built socio-economic attribute database, UP map, and other public service thematic maps such as public transportation, education, child-care, and convenience services. And this research analyzed spatial and attribute data using Pearson's correlation analysis, multiple linear regression, and binary logistic regression methods. As a result of this analysis, 1) the nearer neighborhood park and children's park, the higher land price and assumption income level (AIL). 2) children's parks were closed to living convenience facilities such as bank, hospital, and convenience store. 3) land price, AIL, population, and other public services level (PSL) in UPSA were higher than that of non-UPSA. 4) The higher land price, AIL, population, and other PSL, the higher urban park service level. The results of this research may contribute to resolve the regional UP unbalance and to improve UP service level as public service.

• 한국노년학
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• v.38 no.2
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• pp.351-364
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• 2018

The purpose of this study is to learn about the decision factors affecting utilization of elderly welfare center of the elderly living in Gimpo city. The reason of the study is that the elderly welfare center as a provider of general welfare services could not only thinking about the state policy but also need to consider about the inherent role and function of the elderly. Especially for these elders living in rural areas, although the number of elderly welfare centers of the whole country has greatly increased in last 10 years, the effect and function of the facility are almost the same and they are still lack of leisure activities. This issue become a serious problem nowadays. For the above reasons, this article conducts a social survey of 360 elderly people over the age of 65 who lives in the Gimpo city which is a rural-urban type city. The research method is to examine the relationship between the predisposing factors, enabling factors and need factors of Andersen's behavior model with binary logistic regression analysis and the decision tree analysis. The result of binary logistic regression shows the most of factors of Andersen's model is significant. The factors of age, gender, education level in predisposing factors; monthly income in enabling factors and the reserve for old life, the preparation of economic activity for old life in need factors are significant. Then the result of decision tree analysis shows the interaction between factors; when the education level in predisposing factors is higher, the possibility of using of elderly welfare center becomes bigger. Also as the level of healthy promoting preparation in the need factors gets lower, the possibility of using of elderly welfare center still becomes bigger. Although differences were found in the interpretation of the results of regression analysis and decision tree analysis, the results of this study can still provide support for the necessity of elderly welfare centers providing integrated welfare services.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.685-692
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• 2018

The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.