• Korean Journal of Social Welfare
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• v.68 no.2
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• pp.5-25
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• 2016

Despite previous literatures suggesting the co-occurrence of substance abuse and behavioral addiction, their relationship has not been systematically explored. Especially, college students are a high risk group for alcohol use and gambling activities and they have various psychosocial problems due to addictive behaviors. This study aimed to empirically examine that drinking problems predict gambling problems among college students. A total of 455 college students who experienced drinking and gambling completed a survey. Logistic regression analysis were performed. After adjusting for demographics and family related variables, drinking problems predicted the occurrence of problem gambling. Implications for social work practice, policy planning and research area on addiction are discussed.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Management & Information Systems Review
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• v.37 no.3
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• pp.115-130
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• 2018

The purpose of this study is to clarify the relationship between organizational characteristics and information systems characteristics or job satisfaction, attempts to examine the regulatory effects brought about by the adjustment of social capital theory. So far, The results of this study are based on the analysis of individual models from the perspectives of each functional organization such as HR, organization, finance, operation, and MIS. Therefore, this paper attempted a comprehensive analysis of factors affecting job satisfaction and firm performance by presenting an integrated research model of organizational perspectives in addition to the approach of MIS perspective. The characteristics of information system were promptness, CEO support, and compensation. And the organizational characteristics were multiple regression analysis using innovation, trust, and preferential factors. The analysis data is based on sixth data from the HCCP of Korea Productivity Center. According to the analysis results, all the variables had a significant influence on satisfaction, especially CEO support and trust. The analysis of the moderating effect between innovation and job satisfaction was moderated by vision sharing. Only the logistic regression analysis of the satisfaction with the average salary of the members among the demographic variables was statistically significant. Therefore, this study can be concluded that the overall satisfaction level will be improved by recognizing appropriate compensation as sufficient compensation.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.371-384
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• 2013

The purpose of this study was to develop the risk-adjusted mortality model using Korean Hospital Discharge Injury data and US National Hospital Discharge Survey data and to suggest some ways to manage hospital mortality rates through comparison of Korea and United States Hospital Standardized Mortality Ratios(HSMR). This study used data mining techniques, decision tree and logistic regression, for developing Korea and United States risk-adjustment model of in-hospital mortality. By comparing Hospital Standardized Mortality Ratio(HSMR) with standardized variables, analysis shows the concrete differences between the two countries. While Korean Hospital Standardized Mortality Ratio(HSMR) is increasing every year(101.0 in 2006, 101.3 in 2007, 103.3 in 2008), HSMR appeared to be reduced in the United States(102.3 in 2006, 100.7 in 2007, 95.9 in 2008). Korean Hospital Standardized Mortality Ratios(HSMR) by hospital beds were higher than that of the United States. A two-aspect approach to management of hospital mortality rates is suggested; national and hospital levels. The government is to release Hospital Standardized Mortality Ratio(HSMR) of large hospitals and to offer consulting on effective hospital mortality management to small and medium hospitals.

• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.85-112
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• 2013

This study aims to understand the developmental trajectories of life satisfaction among retirees and to examine what factors differentiate different trajectory classes. This study used three waves of longitudinal data from Korean Retirement and Income Study and data collected every two years(2005, 2007, and 2009). Subjects were respondents aged 50-69 who identified to be retired between wave 1 and wave 2. Finally, this study used 243 respondents for final data analysis. Life satisfaction was measured by seven items. The latent class growth model and multiple logistic regression model were used for data analysis. This study identified three distinct trajectory classes: high stable class(47.7%), high at the early stage but decreased class(42.8%), and low at the early stage and then decreased class(9.5%). This study founded that approximately 50% of the retirees experienced the decline of life satisfaction after retirement and about 10% of the sample was the most vulnerable group. This study analyzed what factors make different among the distinct trajectory groups. As a results, retirees who experienced the improvement in health change were more likely to be in 'high stable class' compared to 'hight at the early stage but decreased class'. In addition, retirees who were less educated, maintained the same health status rather than the improvement, worked as a temporary or a day laborer, and had less household income were more likely to belong to 'low at the early stage and then decreased class' relative to 'high stable class'. This study suggests that there are distinct three trajectories on life satisfaction among the retirees and finds out factors differentiating between trajectory groups. Based on these findings, the study discusses the implications for social work practice and further study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5187-5195
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• 2012

This study was performed to investigate the factors related to assertive behavior among non-smoking college students under secondhand smoke exposure. Data were collected from 246 non-smoking college students at one university in B city from October to November, 2011 and analyzed by multiple logistic regression. The factors related to assertive behavior were men(OR 3.173, 95% CI 1.676-6.005), with another smoker in household(OR 1.679, 95% CI 1.056-2.983), high level of perceived benefit(OR 2.821, 95% CI 1.044-7.623), high level of social influence(OR 3.753, 95% CI 1.845-7.634), high level of self efficacy(OR 4.140, 95% CI 2.159-7.941). It is necessary to develop and evaluate the health promotion program for enhancing of assertive behavior of non-smoking college students regarding of perceived benefit, social influence and self efficacy.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.493-509
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• 2018

This study was performed to compare the effects of health behaviors, dietary habits, and psychological health on metabolic syndrome (MS) between young adults living in one-person households (OPHs) and multiple-person households (MPHs). The data from the Korean National Health and Nutrition Examination Survey (KNHANES), which was conducted in 2014 and 2016 were used. The subjects were 2,682, who were 20 to 39 years old. The data were analyzed using complex sample Rao-Scott ${\chi}^2-tests$, t-tests, and multiple logistic regression using SPSS 23.0 software. Sex, age, obesity, and subjective health status were associated with MS in young adults living in either OPHs or MPHs. Breakfast consumption frequency, eating alone, food label use, stress, and depression were associated with MS only in young adults living in OPHs. Thus, these differentiated risk factors of MS should be considered, when health promotion strategies and interventions are planned for young adults living in OPHs. Also, further studies are needed to evaluate the effectiveness of the strategies or interventions.

• 한국노년학
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• v.31 no.1
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• pp.171-188
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• 2011

The purpose of this study is to examine the factors associated with the probability and levels of the health service utilization among the disabled elderly in Korea. The sample includes 2,111 people older than 65 who are extracted from the 2008 National Survey on People with Disabilities. More than half (54.3%) of the sample experienced at least 1 outpatient physician visit within two weeks and 26.7% were hospitalized within a year. The key factors associated with the outpatient visits were health insurance status, the existence of chronic disease, self-rated health, the Activities of Daily Living (ADLs), as well as renal impairment. Similarly, the utilization of inpatient care was related to health insurance status along with the existence of the internal organ disabilities such as cardiac or respiratory disorders. The study implies the need for the health care policies regarding the prevention of chronic diseases, dependency for daily activities of the elderly, and a management system that specifically targets those with internal organ disabilities. Moreover, the study suggests that financial supports for the low-income group would be helpful to increase their access to health service utilization.

• Journal of the Korean Society of Radiology
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• v.11 no.3
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• pp.131-137
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• 2017

This study investigated the risk factor of Gallbladder stone in Busan and Kyungnam area. The subjects of the experiment was performed with patients by abdominal ultrasonography among the patients who came to the P hospital from June 2016 to December 2016. Among them, risk factors were analyzed on 353 people at the same time when abdominal ultrasonography and hematological test. The statistical analysis of risk factors related to the Gallbladder stone was performed by independent t-test and chi-square test. In consider of difference verification result for calculations odds ratio about independent variables, multiple logistic regression analysis to conduct verify adequacy by calculating forecasting model from variable. As a result, Gallbladder stone risk factors have relevance to age ${\gamma}GTP$ with probability model and values to calculated. Age was showed sensitivity 49.7%, specificity 82.2%, receiver operating characteristic area under curve 0.724. Forecasting probability sensitivity 69.3%, specificity 62.4%, receiver operating characteristic area under curve 0.699 showed, ${\gamma}GTP$ confirmed validity of forecasting model.