• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.63-70
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• 2019

This study examined the effect of emotional factors, physical factors and socioeconomic factors on suicidal ideation of 75 or older seniors and sought to identify what kinds of factors should be satisfied to prevent their suicide. Using "2017 National Survey of the Elderly", the study conducted survey among 75 or older 3,023 males and 1,295 females. It used multivariate logistic regression analysis to identify the factors affecting suicidal ideation. As a result, the study found that ties with their offspring, the number of chronic diseases, being abused or not, depression and living with or without espouse had significant effects on their suicidal ideation and abuse was the largest factor. Therefore, in order to reduce suicide rate of 75 or older seniors, more active attention should be rendered to their physical, socioeconomic and emotional health problems, and measures to reduce elderly abuse should be sought. In particular, institutional improvement and revitalization of elderly counseling institutions are needed to reduce elder abuse

• Journal of Korean Academy of Nursing
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• v.49 no.3
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• pp.329-339
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• 2019

Purpose: The purpose of this study was to investigate the frequency, patterns, and factors of reversals in decisions about life-sustaining treatment (LST) among older patients with terminal-stage chronic cardiopulmonary disease. Methods: This was a retrospective correlational descriptive study based on medical chart review. De-identified patient electronic medical record data were collected from 124 deceased older patients with terminal-stage cardiopulmonary disease who had made reversals of LST decisions in an academic tertiary hospital in 2015. Data were extracted about the reversed LST decisions, LST treatments applied before death, and patients' demographic and clinical factors. Multivariate logistic regression analysis was used to identify the factors associated with the reversal to higher intensity of LST treatment. Results: The use of inotropic agents was the most frequently reversed LST treatment, followed by cardiopulmonary resuscitation, intubation, ventilator therapy, and hemodialysis. Inconsistency between the last LST decisions and actual treatments occurred most often in hemodialysis. One-third of the reversals in LST decisions were made toward higher intensity of LST treatment. Patients who had lung diseases (vs. heart diseases); were single, divorced, or bereaved (vs. married); and had an acquaintance as a primary decision maker (vs. the patients themselves) were significantly more likely to reverse the LST decisions to higher intensity of LST treatment. Conclusion: This study demonstrated the complex and turmoil situation of the LST decision-making process among older patients with terminal-stage cardiopulmonary disease and suggests the importance of support for patients and families in their LST decision-making process.

• Health Policy and Management
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• v.31 no.1
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• pp.24-34
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• 2021

Background: The purpose of this study is to identify factors that affect work dissatisfaction and turnover intention for dedicated nurses working in emergency departments of vulnerable areas of health care. The purpose of this study is to identify risk factors related to the working environment that influence job dissatisfaction and intention to turnover among dedicated nurses working in emergency rooms in areas of medical vulnerability. Methods: We conducted a survey of nurses working in emergency rooms in vulnerable areas of medical care, and the survey was conducted for two consecutive years. A logistic regression analysis was performed with the working environment variable as the independent variable and the work environment dissatisfaction and turnover intention as dependent variables, respectively. Results: The variables that significantly affected both dissatisfactions with the working environment and turnover intention at the current institution were age, overlapping work in other departments, and the total work experience of nurses. Annual salary, the average number of double-duty (continuous work) per month, type of work, and work experience of nurses at the current institution had a significant effect only on dissatisfaction with the working environment. Conclusion: The results of this study are thought to be of great help if the government takes reference when establishing medical policies in vulnerable areas in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.699-704
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• 2022

The purpose of this study is to determine the relating factors of the Budgeting for Korean Medicine health promotion in the Integrated Community Health Promotion Program as of 2020 targeting 226 basic local governments nationwide. Descriptive statistics, Difference tests, and Binary logistic regression analysis were conducted to analyze the factors relating Budgeting for Korean Medicine health promotion in the Integrated Community Health Promotion Program in local communities using regional budgets in 2020. As a result of the analysis, the difference in the Budgeting for Korean Medicine Infertility Treatment Support, Number of Korean Medicine Doctor, Number of Population people over 65 years old, and Aging Population Percentage. And there was a influencing factors, Budgeting for Korean Medicine Infertility Treatment Support, Aging Population Percentage, Whether to join a healthy city, so expected to be used as a reference in organizing policies and business budgets related to Korean Medicine in the future.

• Health Policy and Management
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• v.32 no.2
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• pp.216-227
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• 2022

Background: Food insecurity may contribute to mental health indicators such as stress, anxiety, or depression. We investigated whether food insecurity was associated with mental health indicators and health-related quality of life (HRQoL) in a representative sample of the Korean population. Methods: This study enrolled 12,987 adults without a history of medically serious disease from the 2012, 2013, and 2015 Korea National Health and Nutrition Examination Survey. Household food security status was categorized as "food security," "mild food insecurity," and "moderate/severe food insecurity." The association between mental health and HRQoL was evaluated using a multivariate logistic regression model with food security as the reference group. Results: The adjusted odds ratio of adverse mental health or low HRQoL increased significantly in mild or moderate/severe food insecurity compared to food security. In the moderate/severe food insecurity group, it was 1.98% (95% confidence interval [CI], 1.31-2.99) higher for perceived stress, 3.58% (95% CI, 2.44-5.26) higher for depression symptoms, 4.16% (95% CI, 2.68-6.45) higher for suicidal ideation, and 2.81% (95% CI, 1.91-4.15) higher for quality of life. Conclusion: Food insecurity was strongly associated with negative mental health status and poor HRQoL. There is a need for a dietary support program that provides psychosocial support to those experiencing food insecurity.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.111-120
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• 2020

This study examined the factors affecting the readmission of patients with liver cirrhosis, and focused on self-care, social support, and drinking refusal self-efficacy. The subjects were 75 cirrhosis patients who were admitted to medium-sized hospitals at S-city for two months from May 2019 to June 2019. The data was analyzed with the SPSS (Version 25) program, and logistic regression analysis was performed on the factors affecting readmission. The results were self-care (27.49±0.53 out of 60), social support (52.80±16.44 out of 90), and drinking refusal self-efficacy (42.39±22.76 out of 80). The readmission method was classified into planned and unplanned admissions. Unplanned readmission was found to differ depending on the drinking experience (OR: 4.16) and the presence of complications (OR: 5.11) within a month of discharge rather than that of the planned readmissions, accounted for 19.7%. It will be very important to reduce the occurrence of complications by early management of patients with cirrhosis, and increase the drinking refusal self-efficacy, and so reduce unplanned readmission and prevent the progression and deterioration of cirrhosis. The drinking experience and the occurrence of complications can be reduced through interventions that increase self-care, social support, and drinking refusal self-efficacy. Nursing interventions are needed to prevent patients with cirrhosis from drinking and to manage the complications due to relapse into alcoholism.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.173-190
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• 2015

The purpose of this study is to improve bankruptcy prediction models by using a novel hybrid under-sampling approach. Most prior studies have tried to enhance the accuracy of bankruptcy prediction models by improving the classification methods involved. In contrast, we focus on appropriate data preprocessing as a means of enhancing accuracy. In particular, we aim to develop an effective sampling approach for bankruptcy prediction, since most prediction models suffer from class imbalance problems. The approach proposed in this study is a hybrid under-sampling method that combines the k-Reverse Nearest Neighbor (k-RNN) and one-class support vector machine (OCSVM) approaches. k-RNN can effectively eliminate outliers, while OCSVM contributes to the selection of informative training samples from majority class data. To validate our proposed approach, we have applied it to data from H Bank's non-external auditing companies in Korea, and compared the performances of the classifiers with the proposed under-sampling and random sampling data. The empirical results show that the proposed under-sampling approach generally improves the accuracy of classifiers, such as logistic regression, discriminant analysis, decision tree, and support vector machines. They also show that the proposed under-sampling approach reduces the risk of false negative errors, which lead to higher misclassification costs.