• Journal of the East Asian Society of Dietary Life
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• v.18 no.4
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• pp.608-617
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• 2008

This study examined the level of reaction towards damage related to health foods and the factors affecting this reaction among the elderly. Data were collected from 269 elderly individuals living in Seoul, Daejeon, Daegu, Gwangju and Busan. Frequencies, chi-square tests, and a multinomial logistic regression analysis were performed using the SPSS v. 14.0 program. When asked about their reaction towards damage related to health foods, approximately 48% of the respondents answered 'no response', 34% answered 'private response', and 18% answered 'public response'. Multinomial logistic regression analysis revealed that education level and awareness of health food price were significant factors influencing 'private response', and concerns about health foods and awareness of damage redemption were significant factors for 'public response'. These results imply that consumer education for elderly to prevent damage derived from the purchase and consumption of health foods is required.

• Earthquakes and Structures
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• v.26 no.2
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• pp.87-96
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• 2024

This paper proposes a logistic multinomial regression approach to model the spatial cross-correlation of damage probabilities among different damage states in an expanded transportation network. Utilizing Bayesian theory and the multinomial logistic model, we analyze the damage states and probabilities of bridges while incorporating damage correlation. This correlation is considered both between bridges in a network and within each bridge's damage states. The correlation model of damage probabilities is applied to the seismic assessment of a portion of Tehran's transportation network, encompassing 26 bridges. Additionally, we introduce extra daily traffic time (EDTT) as an operational parameter of the transportation network and employ the shortest path algorithm to determine the path between two nodes. Our results demonstrate that incorporating the correlation of damage probabilities reduces the travel time of the selected network. The average decrease in travel time for the correlated case compared to the uncorrelated case, using two selected EDTT models, is 53% and 71%, respectively.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Korea Trade Review
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• v.47 no.2
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• pp.69-88
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• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• The Korean Journal of Pain
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• v.36 no.2
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• pp.230-241
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• 2023

Background: This study aimed to determine the prevalence of chronic pain and its contributing factors among teenagers aged 12-21 years in Shiraz, Iran. Methods: This cross-sectional study was conducted on adolescents aged 12-21 years. Demographic variables of the adolescents and their parents as well as the pain characteristics were assessed. Descriptive statistics, multinomial logistic regression, and regression models were used to describe the characteristics of the pain and its predictive factors. Results: The prevalence of chronic pain was 23.7%. The results revealed no significant difference between the male and female participants regarding the pain characteristics, except for the home medications used for pain relief. The results of a chi-square test showed that the mother's pain, education, and occupation, and the father's education were associated significantly with chronic pain in adolescents (P < 0.05). Multinomial logistic regression also showed the mother's history of pain played a significant role in the incidence of adolescents' chronic pain. Conclusions: The prevalence of chronic pain was relatively high in these adolescents. The results also provided basic and essential information about the contributing factors in this area. However, consideration of factors such as anxiety, depression, school problems, sleep, and physical activity are suggested in future longitudinal studies.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.4
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• pp.1627-1631
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• 2015

Background: Stage at diagnosis is one of the most important prognostic factors of breast cancer survival. Because in the breast cancer case this may vary with socioeconomic characteristics, this study was performed to recognize the relationship between demographic and socioeconomic factors with stage at diagnosis in Iran. Materials and Methods: This cross-sectional, descriptive study conducted on 526 patients suffering from breast cancer and registered in Cancer Research Center of Shahid Beheshti University of Medical Sciences from 2008 to 2013. A reliable and valid questionnaire about family levels of socioeconomic status filled in by interviewing the patients via phone. For analyzing the data, Multinomial logistic regression, Kendal tau-b correlation coefficient and Contingency Coefficient tests were executed by SPSS22. Economic status, educational attainment of patient and household head and/or a combination of these were considered as parameters for socioeconomic status. First, the relationship between stage at diagnosis and demographic and socioeconomic status was assessed in univariate analysis then these relationships assessed in two different models of multinomial logistic regression. Results: The mean age of the patients was 48.3 (SD=11.4). According to the results of this study, there were significant relationships between stage at diagnosis of breast cancer with patient education (p=0.011), living place (p=0.044) and combined socioeconomic status (p=0.024). These relationships persisted in multiple multinomial logistic regressions. Other variables, however, had no significant correlation. Conclusions: Patient education, combined socioeconomic status and living place are important variables in stage at diagnosis of breast cancer in Iranian women. Interventions have to be applied with the aim of raising women's accessibility to diagnostic and medical facilities and also awareness in order to reducing delay in referring. In addition, covering breast cancer screening services by insurance is recommended.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.424-429
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• 2007

The purpose of this study is to propose types of joint venturesthat can increase the competitivenessof a company in the marketplace. We examine the characteristics of individual venture enterprises based on technology. We considered 16 TEA in order to categorize companies into four groups. Next, we used a multinomial logistic regression model to identify the significant characteristics of a venture company that successfully predicts group membership. Based on this information, we propose various forms of joint venture which complement each other and produce higher overall competence. Our study can provide important feedback information to academics, Policy-makers.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.97-107
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• 2018

PURPOSES : The effects on traffic accidents change with the changing environment. Accordingly, this study analyzes the characteristics of traffic accidents based on the personal characteristics (gender and age) of drivers, and those of 25 autonomous districts in Seoul, and suggests improvements. METHODS : Based on data pertaining to traffic accidents in Seoul, the analysis of accident characteristics was conducted by categorizing the types of traffic accidents according to the drivers' gender and age, and characteristics of 25 autonomous districts in Seoul. Further, for statistical verification, the SPSS software was used to derive influence variables through a multinomial logistic regression analysis, and a method for reducing traffic accidents was proposed. RESULTS : Analysis results show that males tend to be more involved in speed-related accidents and females in low-experience driving-related accidents such as those during parking and alleyway driving. In addition, variables such as age, automobile type, district, and day of the week are found to influence accident types. CONCLUSIONS : This study analyzed the accident characteristics based on personal and city characteristics to reflect the sociological characteristics that influence traffic accidents. The number of traffic accidents in Korea could be decreased drastically by implementing the results of this study in customized safety education and traffic maps.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.265-285
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• 2018

In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.