• Health Policy and Management
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• v.33 no.4
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• pp.400-410
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• 2023

Background: As the government has recently been discussing the expansion of the disaster health expenses support project, we would like to confirm the characteristics of beneficiaries of the support project, particularly those of high-cost beneficiaries. Methods: Using the database of catastrophic health expenditure support project from 2019-2020, this study aims to confirm the characteristics of high-cost beneficiaries focusing on the overlap of the relieved out-of-pocket systems, known as the out-of-pocket ceiling system and the system for rare incurable diseases. Logistic regression analysis is used to examine this issue. Results: In order to analyze the factors influencing high-cost beneficiaries, five models were created and analyzed, including the status of duplicated beneficiaries for relieved out-of-pocket systems, sociodemographic and economic factors, and individual health status as sequential independent variables. All five models were statistically significant, of which economic factors had the greatest impact on the model's predictions. The main results indicated that those who benefited from multiple systems in duplicate were more likely to be high-cost beneficiaries, and there is a higher probability of incurring high health expenses among the underage. In addition, within the beneficiaries of catastrophic health expenditure support project, it was observed that higher health insurance premium percentiles are associated with a higher proportion of high-cost beneficiaries. Conclusion: This study examined the characteristics of high-cost beneficiaries by encompassing reimbursement and non-reimbursement. According to this study, it is expected to be used as basic data for setting priorities and improving the current criteria of catastrophic health expenditure support project, aiming to sequentially expand the program.

• Korean Journal of Adult Nursing
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• v.21 no.6
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• pp.580-592
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• 2009

Purpose: The purpose of this study was to investigate the factors related to Breast cancer Screening using Mammography and CBE of Korean women over 40 years of age. Methods: The participants for this study were 183 Korean women living in 3 urban cities and aged from 40 to 75. The data were collected using structured questionnaires which included sociodemographic factors (11 items), frequency and regularity of mammography and clinical breast examination (7 items), knowledge (16 items), health belief model scale (28 items), and family support (4 items) about breast cancer and breast cancer screening. Frequencies, Chronbach's alpha for reliability, Chi-square, t-test and logistic regression with the SPSS/WIN 12 program were used to analyze the data. Results: The percentages of Korean women who had a mammography and CBE for breast cancer screening were 60.1 and 31.1, respectively. Logistic regression analyses demonstrated that regular check ups and perceived barriers were significant predictors of mammography and CBE use for breast cancer screening. Conclusion: In order to increase the frequency of breast cancer screening practices, educational support and a health care delivery system is needed to improve the chance of regular health check ups.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.3933-3938
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• 2014

Background: Assessment of supportive needs is the requirement to plan any supportive care program for cancer patients. There is no evidence about supportive care needs of Iranian breast cancer patients. So, the aims of present study were to investigate this question and s predictive factors. Materials and Methods: A descriptive-correlational study was conducted, followed by logistic regression analyses. The Supportive Care Needs Survey was completed by 136 breast cancer patients residing in Iran following their initial treatment. This assessed needs in five domains: psychological, health system and information, physical and daily living, patient care and support, and sexuality. Results: Patient perceived needs were highest in the health systems and information (71%), and physical and daily living (68%) domains. Logistic regression modeling revealed that younger participants have more un-met needs in all domains and those with more children reported fewer un-met needs in patient care and support domains. In addition, married women had more un-met supportive care needs related to sexuality. Conclusions: The high rate of un-met supportive care needs in all domains suggests that supportive care services are desperately required for breast cancer patients in Iran. Moreover, services that address informational needs and physical and daily living needs ought to be the priority, with particular attention paid to younger women. Further research is clearly needed to fully understand supportive care needs in this cultural context.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• Research in Community and Public Health Nursing
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• v.26 no.3
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• pp.179-189
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• 2015

Purpose: The purpose of this study was to identify factors influencing depression of participants in the National Supporting Program for Infertile Couples (NSPI) who received a treatment of IVF (in vitro fertilization) or IUI (intra-uterine insemination). Methods: Using the 2013 NSPI Satisfaction On-line Survey data, secondary data analyses were conducted on 830 cases of IVF and 706 cases of IUI. Descriptive statistics, independent t-test, ${\chi}^2$ test, and logistic regression were performed using SPSS/WIN 21.0. Results: Logistic regression analysis showed that non-pregnancy status (OR=3.05), unexplained infertility (OR=4.29), relationship trouble with spouse (OR=3.57), and relationship trouble with the in-law family (OR=2.78) were significant factors predicting the depression level in the IUI treatment group. Non-pregnancy status (OR=5.28), childlessness (OR=1.92), financial support helpful hardly or not at all (OR=2.63), relationship trouble with spouse (OR=3.28), relationship trouble with the in-law family (OR=2.83), and unemployment (OR=1.60) were significant factors in the IVF treatment group. Conclusion: To reduce infertile women's depression, adequate attention and care need to be paid to these psychological symptoms. It is suggested to develop counseling and couple-therapy along with methods to enhance social support (including that from the in-law's family).

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1341-1348
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• 2013

To solve the classification problems, various data mining techniques have been applied to database marketing, credit scoring and market forecasting. In this paper, we compare various techniques such as bagging, boosting, LASSO, random forest and support vector machine with the daily lens transaction data. The classical techniques-decision tree, logistic regression-are used too. The experiment shows that the random forest has a little smaller misclassification rate and standard error than those of other methods. The performance of the SVM is good in the sense of misclassfication rate and bad in the sense of standard error. Taking the model interpretation and computing time into consideration, we conclude that the LASSO gives the best result.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.249-258
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• 2015

This study was conducted to determine the affecting factors among patients with drinking behavior of liver cirrhosis. Data were collected by questionnaire from 157 patients who were diagnosed with liver cirrhosis at a tertiary hospital located in D-city. Measurements included patients' demographic characteristics, clinical characteristics, disease-related of symptom experience, emotional-factors of anxiety-depression and social-factor of social support. Data were analyzed using t-test, and logistic regression analyses. The incidence rate of drinking behavior was 31.8%. Multivariate analysis revealed that smoking(yes), men, symptom experience, social support and anxiety were more likely to report high level of drinking behavior. Future research should consider managing drinking behavior as an essential component of comprehensive care for patients with liver cirrhosis.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.133-140
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• 2018

Accurate sentiment classification is an important research topic in sentiment analysis. This study suggests an efficient classification method of Korean sentiment using word2vec and ensemble methods which have been recently studied variously. For the 200,000 Korean movie review texts, we generate a POS-based BOW feature and a feature using word2vec, and integrated features of two feature representation. We used a single classifier of Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine and an ensemble classifier of Adaptive Boost, Bagging, Gradient Boosting, and Random Forest for sentiment classification. As a result of this study, the integrated feature representation composed of BOW feature including adjective and adverb and word2vec feature showed the highest sentiment classification accuracy. Empirical results show that SVM, a single classifier, has the highest performance but ensemble classifiers show similar or slightly lower performance than the single classifier.

• Advances in aircraft and spacecraft science
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• v.1 no.2
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• pp.197-218
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• 2014

Quite a demanding task frequently arises in space engineering, when dealing with the cargo accommodation of modules and vehicles. The objective of this effort usually aims at maximizing the loaded cargo, or, at least, at meeting the logistic requirements posed by the space agencies. Complex accommodation rules are supposed to be taken into account, in compliance with strict balancing conditions and very tight operational restrictions. The context of the International Space Station (ISS) has paved the way for a relevant research and development activity, providing the company with a remarkable expertise in the field. CAST (Cargo Accommodation Support Tool) is a dedicated in-house software package (funded by the European Space Agency, ESA, and achieved by Thales Alenia Space), to carry out the whole loading of the Automated Transfer Vehicle (ATV). An ad hoc version, tailored to the Columbus (ISS attached laboratory) on-board stowage issue, has been further implemented and is to be used from now on. This article surveys the overall approach followed, highlighting the advantages of the methodology put forward, both in terms of solution quality and time saving, through an overview of the outcomes obtained to date. Insights on possible extensions to further space applications, especially in the perspective of the paramount challenges of the near future, are, in addition, presented.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.81-107
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• 2009

The Korean government provides a variety of supporting programs with firms to promote technological innovation which is a main driver of economic growth. The existing literature on technological innovation has mainly focused on analysis about determinants of successful innovation and effectiveness of a specific policy measure. However, there is no study deals with characteristics of policy demanders. For this, this study investigates the relationship between barriers and policy measures in technological innovation using the logistic regression analysis method with raw data of Korean Innovation Survey (KIS). The findings from this analysis show that barriers of technological innovation are meaningful variables to determining whether firms adopt a policy measure, although there are some differences according to policy types. Cost barriers increased the probability that firms adopt support programs regardless of policy types. Also, the more firms encounter cooperation barriers, the more likely firms utilize supporting programs in regard to technological advice and information.