• Korean Journal of Radiology
• /
• v.25 no.7
• /
• pp.644-655
• /
• 2024

Objective: To develop and validate a preoperative risk score incorporating carbohydrate antigen (CA) 19-9, CT, and fluorine18-fluorodeoxyglucose (¹⁸F-FDG) PET/CT variables to predict recurrence-free survival (RFS) after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: Patients with resectable PDAC who underwent upfront surgery between 2014 and 2017 (development set) or between 2018 and 2019 (test set) were retrospectively evaluated. In the development set, a risk-scoring system was developed using the multivariable Cox proportional hazards model, including variables associated with RFS. In the test set, the performance of the risk score was evaluated using the Harrell C-index and compared with that of the postoperative pathological tumor stage. Results: A total of 529 patients, including 335 (198 male; mean age ± standard deviation, 64 ± 9 years) and 194 (103 male; mean age, 66 ± 9 years) patients in the development and test sets, respectively, were evaluated. The risk score included five variables predicting RFS: tumor size (hazard ratio [HR], 1.29 per 1 cm increment; P < 0.001), maximal standardized uptake values of tumor ≥ 5.2 (HR, 1.29; P = 0.06), suspicious regional lymph nodes (HR, 1.43; P = 0.02), possible distant metastasis on ¹⁸F-FDG PET/CT (HR, 2.32; P = 0.03), and CA 19-9 (HR, 1.02 per 100 U/mL increment; P = 0.002). In the test set, the risk score showed good performance in predicting RFS (C-index, 0.61), similar to that of the pathologic tumor stage (C-index, 0.64; P = 0.17). Conclusion: The proposed risk score based on preoperative CA 19-9, CT, and ¹⁸F-FDG PET/CT variables may have clinical utility in selecting high-risk patients with resectable PDAC.

• Proceedings of the Safety Management and Science Conference
• /
• 2013.11a
• /
• pp.275-290
• /
• 2013

The main cause of building fire fatalities occur in the combustible material heat, smoke and toxic gases are. Building interior decoration, etc., especially as much of the harmful substances generated during combustion, and, used in domestic architecture wallpaper, ceiling, and other plastics, built-in foam insulation also analyzed recognition of fire hazards approach to test the conkalrorimiteo test, choedaeyeolbangchulryul through, chongbal heat, mass loss rate, generates carbon monoxide gas hazard ratio tests, analysis and evaluation rigid foam index testing the toxicity of hazardous material generated by performing a gas clean up and assess the material test results, the minimum order to provide data to quantify the risk of fire. Ensure fire safety of building materials, composite materials in order to test the various risk factors could be considered organic to the introduction of testing and evaluation is needed urgently.

• Journal of The Korean Society of Integrative Medicine
• /
• v.12 no.2
• /
• pp.155-166
• /
• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• The Journal of Industrial Distribution & Business
• /
• v.13 no.10
• /
• pp.1-8
• /
• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• Korean Journal of Health Education and Promotion
• /
• v.19 no.2
• /
• pp.45-55
• /
• 2002

This study was conducted to evaluate the degree of stress state and stress related factors in 280 male white collar workers by using Psychosocial Well-being Index. The results were as follows; 1. According to Psychosocial Well-being Index, mild stress state was 78.6 %, healthy state was 12.9 %, and high risk stress state was 8.6 %. Single marital status, low education level, low income and low frequency of exercise group had high score of stress. 2. The total stress score was highly associated with the factors of social performance and self confidence, depression, general well-being and vitality, and sleeping disturbance and anxiety in order. 3. In reliability test of stress factors, Cronbach's a coefficients of social performance and self confidence, sleeping disturbance and anxiety, depression, general well-being and vitality were 0.91, 0.91, 0.90, and 0.89 respectively. In conclusion, it suggested that marital status, income, education, and exercise were associated with stress score. All of the above factors should be considered to white collar workers health.

• Journal of the Korean Society of School Health
• /
• v.36 no.3
• /
• pp.113-125
• /
• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.9
• /
• pp.105-115
• /
• 2020

The study empirically examines the effects of loan portfolio diversification on bank risk and return in the nascent banking market of Vietnam. Loan portfolio diversification is captured through the Hirschman-Herfindahl index and the Shannon Entropy with sectoral exposures. We access each bank's financial reports to collect the required data, especially the breakdown of sectoral loan portfolios, thus constituting a unique dataset. To compute bank return, we use the traditional accounting indicators, including return-on-assets, return-on-equity, and net-interest margin. For bank risk, we utilize the loan-loss provisions and non-performing loans relative to gross customer loans. Using a sample of 30 commercial banks over the period from 2008 to 2019 and the system generalized method of moments estimator for the dynamic panel, we indicate the downsides of portfolio diversification. Concretely, we observe that all diversification measures exhibit significantly negative signs in all regressions across different bank return proxies. At the same time, the estimates display the significant and positive impact of diversification on the non-performing loan ratio. Hence, sectoral loan portfolio diversification significantly hampers bank performance in both aspects of lower return and higher credit risk. The results are robust across a rich set of bank performance and portfolio diversification measures.

• Korean Journal of Radiology
• /
• v.25 no.2
• /
• pp.189-198
• /
• 2024

Objective: To investigate the prognostic utility of radiomics features extracted from ¹⁸F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent ¹⁸F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

• Architectural research
• /
• v.21 no.3
• /
• pp.69-77
• /
• 2019

Architects are facing increasing risks that result from heightened expectations of benefits and performance when designing green buildings compared to traditional buildings. This study aims to explore the possible risk factors for architects in green building projects in South Korea and assess risk mitigation measures. To attain this goal, 14 risk factors and 12 mitigation measures were determined through an extensive literature review. A questionnaire was administered to architects practicing green building design and criticality index was employed to assess major risk factors and mitigation measures. This study identified 'adoption of new technology and process', 'green building certification results', 'building products and materials', and 'energy saving uncertainty' as the major risk factors of green building projects. Additionally, the questionnaire proposed 'contract indicating each party's role, liability, and limitations clearly', 'utilizing integrated design process', and 'understanding client's goal in green building projects' as the three most effective risk mitigation measures in designing green buildings. There are few studies that focus on architects' perceived risks concerning green building projects; this study contributes to a deeper knowledge and attempts to fill the current literature gap, which would benefit South Korea's green building design practice by aiding in the development of better risk management strategies.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1285-1298
• /
• 2019

Drought is one of the factors that can cause wildfires. Drought is related to not only the occurrence of wildfires but also their frequency, extent and severity. In South Korea, most wildfires occur in dry seasons (i.e. spring and autumn), which are highly correlated to drought events. In this study, we examined the relationship between wildfire occurrence and drought factors, and developed satellite-based new drought indices for assessing wildfire risk over South Korea. Drought factors used in this study were high-resolution downscaled soil moisture, Normalized Different Water Index (NDWI), Normalized Multi-band Drought Index (NMDI), Normalized Different Drought Index (NDDI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI) and Vegetation Condition Index (VCI). Drought indices were then proposed through weighted linear combination and one-class support vector machine (One-class SVM) using the drought factors. We found that most drought factors, in particular, soil moisture, NDWI, and PCI were linked well to wildfire occurrence. The validation results using wildfire cases in 2018 showed that all five linear combinations produced consistently good performance (> 88% in occurrence match). In particular, the combination of soil moisture and NDWI, and the combination of soil moisture, NDWI, and precipitation were found to be appropriate for representing wildfire risk.