• Korean Journal of Radiology
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• 제25권1호
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

• 유통과학연구
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• 제12권11호
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• pp.77-83
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• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• Journal of Korea Trade
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• 제23권4호
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• pp.17-29
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• 2019

Purpose - This study examines the relationship between public-private partnerships and the performance of ports based on three factors: the quality of the port infrastructure, container throughput, and logistic performance in three Latin American countries, Mexico, Panama, and Brazil, for the period of 1994-2017. Design/methodology - The selected countries are top ranked in terms of container throughput in Latin America. The methodology employs secondary data from the World Bank (Quality of Port Infrastructure, Logistics Performance Index, and Private Participation in infrastructure database). Findings - Overall, the results revealed that the private investment of these countries varies significantly over the past couple decades. Panama, with the least public-private investment over the study period, performs better than Mexico and Brazil with regards to port quality infrastructure and container throughput. For ports in the selected countries to keep up with global competition, there is a need to enhance efficiency. Originality/value - Compared with ports in Asia, Latin American ports are lagging behind with respect to container throughput and efficiency. This study suggests greater collaboration from the private sector, academia, and other organizations, as well as a review of the regulatory framework to ensure better transparency and project allocation. Throwing more light on the public-private investment environment of Mexico, Brazil, and Panama, this study offers policy makers and regulators insightful information on port infrastructure.

• 대한통합의학회지
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• 제12권2호
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• pp.155-166
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• 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.

• 지능정보연구
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• 제23권4호
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• pp.147-168
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• 2017

오래 전부터 학계에서는 정확한 주식 시장의 예측에 대한 많은 연구가 진행되어 왔고 현재에도 다양한 기법을 응용한 예측모형들이 연구되고 있다. 특히 최근에는 딥러닝(Deep-Learning)을 포함한 다양한 기계학습기법(Machine Learning Methods)을 이용해 주가지수를 예측하려는 많은 시도들이 진행되고 있다. 전통적인 주식투자거래의 분석기법으로는 기본적 분석과 기술적 분석방법이 사용되지만 보다 단기적인 거래예측이나 통계학적, 수리적 기법을 응용하기에는 기술적 분석 방법이 보다 유용한 측면이 있다. 이러한 기술적 지표들을 이용하여 진행된 대부분의 연구는 미래시장의 (보통은 다음 거래일) 주가 등락을 이진분류-상승 또는 하락-하여 주가를 예측하는 모형을 연구한 것이다. 하지만 이러한 이진분류로는 추세를 예측하여 매매시그널을 파악하거나, 포트폴리오 리밸런싱(Portfolio Rebalancing)의 신호로 삼기에는 적합치 않은 측면이 많은 것 또한 사실이다. 이에 본 연구에서는 기존의 주가지수 예측방법인 이진 분류 (binary classification) 방법에서 주가지수 추세를 (상승추세, 박스권, 하락추세) 다분류 (multiple classification) 체계로 확장하여 주가지수 추세를 예측하고자 한다. 이러한 다 분류 문제 해결을 위해 기존에 사용하던 통계적 방법인 다항로지스틱 회귀분석(Multinomial Logistic Regression Analysis, MLOGIT)이나 다중판별분석(Multiple Discriminant Analysis, MDA) 또는 인공신경망(Artificial Neural Networks, ANN)과 같은 기법보다는 예측성과의 우수성이 입증된 다분류 Support Vector Machines(Multiclass SVM, MSVM)을 사용하고, 이 모델의 성능을 향상시키기 위한 래퍼(wrapper)로서 유전자 알고리즘(Genetic Algorithm)을 이용한 최적화 모델을 제안한다. 특히 GA-MSVM으로 명명된 본 연구의 제안 모형에서는 MSVM의 커널함수 매개변수, 그리고 최적의 입력변수 선택(feature selection) 뿐만이 아니라 학습사례 선택(instance selection)까지 최적화하여 모델의 성능을 극대화 하도록 설계하였다. 제안 모형의 성능을 검증하기 위해 국내주식시장의 실제 데이터를 적용해본 결과 ANN이나 CBR, MLOGIT, MDA와 같은 기존 데이터마이닝 기법들이나 인공지능 알고리즘은 물론 현재까지 가장 우수한 예측 성과를 나타내는 것으로 알려져 있던 전통적인 다분류 SVM 보다도 제안 모형이 보다 우수한 예측성과를 보임을 확인할 수 있었다. 특히 주가지수 추세 예측에 있어서 학습사례의 선택이 매우 중요한 역할을 하는 것으로 확인 되었으며, 모델의 성능의 개선효과에 다른 요인보다 중요한 요소임을 확인할 수 있었다.

• Korean Journal of Radiology
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• 제21권4호
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• pp.413-421
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• 2020

Objective: A widely applicable, non-invasive screening method for non-alcoholic fatty liver disease (NAFLD) is needed. We aimed to develop and validate an index combining computed tomography (CT) and routine clinical data for screening for NAFLD in a large cohort of adults with pathologically proven NAFLD. Materials and Methods: This retrospective study included 2218 living liver donors who had undergone liver biopsy and CT within a span of 3 days. Donors were randomized 2:1 into development and test cohorts. CTL-S was measured by subtracting splenic attenuation from hepatic attenuation on non-enhanced CT. Multivariable logistic regression analysis of the development cohort was utilized to develop a clinical-CT index predicting pathologically proven NAFLD. The diagnostic performance was evaluated by analyzing the areas under the receiver operating characteristic curve (AUC). The cutoffs for the clinical-CT index were determined for 90% sensitivity and 90% specificity in the development cohort, and their diagnostic performance was evaluated in the test cohort. Results: The clinical-CT index included CTL-S, body mass index, and aspartate transaminase and triglyceride concentrations. In the test cohort, the clinical-CT index (AUC, 0.81) outperformed CTL-S (0.74; p < 0.001) and clinical indices (0.73-0.75; p < 0.001) in diagnosing NAFLD. A cutoff of ≥ 46 had a sensitivity of 89% and a specificity of 41%, whereas a cutoff of ≥ 56.5 had a sensitivity of 57% and a specificity of 89%. Conclusion: The clinical-CT index is more accurate than CTL-S and clinical indices alone for the diagnosis of NAFLD and may be clinically useful in screening for NAFLD.

• 정신신체의학
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• 제25권1호
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• pp.56-62
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• 2017

Objectives : The objective of this study was to investigate the relationship between psychological distress and pain in cancer patients. Methods : 249 patients with cancer who visited National Health Insurance Service Ilsan Hospital between April 2013 and March 2014 were evaluated with National Cancer Center Psychological Symptom Inventory(NCC-PSI) which consisted of Modified Distress Thermometer(MDT) and Modified Impact Thermometer(MIT). Each scale was divided into 3 subscales targeting separate symptoms: insomnia, anxiety, and depression. Psychological distress was defined as positive for those who scored above the cutoff values in at least one of all six subscales. The Numeric Rating Scale for Pain(NRS-Pain) was used to assess the subjective severity of pain. Logistic regression was performed to investigate the association between psychological distress and pain. Results : Univariate logistic regression analysis showed that pain, gender, compliance, and two subscale scores of Hospital Anxiety and Depression Scale(HADS) were significantly associated with psychological distress. Multivariate logistic regression analysis showed that pain and HADS anxiety subscale score maintained a statistically significant association with psychological distress adjusted for variables including age, gender, years of education, Eastern Cooperative Oncology Group performance status, cancer stage, Charlson Comorbidity Index, compliance, and HADS depression subscale score. One point increase in pain was 1.31 times more likely to cause psychological distress. In secondary analysis, pain was significantly associated with all subscales of NCC-PSI, except MIT-anxiety subscale. Conclusions : This study suggests that NCC-PSI, a screening tool for psychological distress, reflects pain. We recommend that physicians who treat cancer patients consider the examination of psychological distress which provides comprehensive evaluation of various factors regarding quality of life.

• Journal of Preventive Medicine and Public Health
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• 제43권1호
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• pp.42-49
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• 2010

Objectives: To compare the performance of three International Statistical Classification of Diseases, 10th Revision translations of the Charlson comorbidities when predicting in-hospital among patients with myocardial infarction (MI). Methods: MI patients ${\geq}20$ years of age with the first admission during 2006 were identified(n=20,280). Charlson comorbidities were drawn from Heath Insurance Claims Data managed by Health Insurance Review and Assessment Service in Korea. Comparisions for various conditions included (a) three algorithms (Halfon, Sundararajan, and Quan algorithms), (b) lookback periods (1-, 3- and 5-years), (c) data range (admission data, admission and ambulatory data), and (d) diagnosis range (primary diagnosis and first secondary diagnoses, all diagnoses). The performance of each procedure was measured with the c-statistic derived from multiple logistic regression adjusted for age, sex, admission type and Charlson comorbidity index. A bootstrapping procedure was done to determine the approximate 95% confidence interval. Results: Among the 20,280 patients, the mean age was 63.3 years, 67.8% were men and 7.1% died while hospitalized. The Quan and Sundararajan algorithms produced higher prevalences than the Halfon algorithm. The c-statistic of the Quan algorithm was slightly higher, but not significantly different, than that of other two algorithms under all conditions. There was no evidence that on longer lookback periods, additional data, and diagnoses improved the predictive ability. Conclusions: In health services study of MI patients using Health Insurance Claims Data, the present results suggest that the Quan Algorithm using a 1-year lookback involving primary diagnosis and the first secondary diagnosis is adequate in predicting in-hospital mortality.

• Asian Pacific Journal of Cancer Prevention
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• 제17권9호
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• pp.4427-4432
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• 2016

Background: Fear and fatalism have been proposed as factors affecting breast cancer screening, but the evidence is not strong. This study aimed to determine relationships of fear and fatalism with breast cancer screening behavior among Tabriz women in Iran. Materials and Methods: In a cross- sectional study, 370 women referred to 12 health centers in Tabriz were selected with two-stage cluster sampling and data regarding breast cancer screening, fatalism and fear of breast cancer were collected respectively with a checklist for screening performance, Champions Fear and Pow Fatalism Questionnaires. Data were analyzed by logistic regression with SPSS software version 16. Results: Only 43% and 23% of participants had undergone breast self- examination and clinical breast examination. Among women older than 40 years, 38.2% had mammography history and only 2.7% of them had done it annually. Although fatalism and fear had a stimulating effects on breast cancer screening performance th relationships were not significant (P>0.05). There was a negative significant correlation between fear and fatalism (r= -0.24, p=0.000). On logistic regression analysis, age (OR=1.037, p<0.01) and income status (OR= 0.411, p<0.05) significantly explained BSE and age (OR=1.051, p<0.01) and body mass index (OR= 0.879, p<0.01) explained CBE. Also BMI (OR= 0.074, p<0.05) and income status (OR=0.155, p<0.01) was significantly effective for mammography following. Conclusions: Breast cancer screening behavior is inappropriate and affected by family livelihood status and lifestyle leads to weight gain, so that for promoting of screening behaviors, economic support to families, lifestyle modification and public education are suggested.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.87-100
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• 2024

Modern logistics significantly influences the globalization and internationalization processes. Logistics systems are becoming increasingly important in today's rapidly changing environment. On the other hand, the development of global economic integration, business globalization contributes to the creation and development of international logistics systems and global supply chains towards the international market. The aim of the article was to investigate the national logistics systems of developing countries in the context of their integration capabilities. The main methods used in this study are statistical analysis, index, graphical and analytical methods, methods for estimating structural dynamic shifts, comparisons. Commonly used methods of economic research, as well as statistical analysis and interstate comparisons, economic modelling (trend analysis to determine the forecast level of LPI for Ukraine), etc. were also involved. It is noted that the problem of development of logistics systems in developing countries was insufficiently covered in scientific research. The study suggests that the integration capabilities of national logistics systems are determined by the logistics performance of each country and the favourable logistics environment for integration transformations. This allowed analysing the state of the logistics systems of Poland, Bulgaria, India and Ukraine, and identifying the factors that determine it. The logistics environment of Poland, Bulgaria, India and Ukraine, as well as the factors of its formation are evaluated. The components of the logistic portrait of the country in the context of integration capabilities of the logistic system are offered. Trend analysis of LPI was carried out on the example of Ukraine, which showed positive trends in the logistics system and allowed drawing conclusions about increasing integration into international logistics channels based on its geopolitical location, improving the characteristics of the logistics environment, including customs regulation, and improving the efficiency of the national logistics system. Prospects for further research involve studies of the impact of pandemics, globalization, digitalization on logistics systems, including that of developing countries.