• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.399-408
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• 2022

The purpose of this study was to compare the usefulness of the lipid ratio indicators for the diagnosis of metabolic syndrome in the elderly aged 65 years or older. From January 2018 to December 2020, 1,464 people aged 65 years or older who underwent a health checkup at a general hospital in Seoul were included. Lipid ratio indicators were measured through blood tests. The prevalence of metabolic syndrome according to the quartiles of the lipid ratio index was confirmed by logistic regression analysis. In addition, the metabolic syndrome predictive ability and cutoff value of the lipid ratio indices were estimated with the receiver operating characteristic(ROC) curve. The correlation between atherogenic index of plasma(AIP) and waist circumference was the highest in both men and women(r=0.278, p<0.001 vs r=0.252, p<0.001). As for the lipid ratio indices, the incidence of metabolic syndrome was higher in the fourth quartile than in the first quartile. The area under the ROC curve(AUC) value of AIP was higher at 0.826(95% CI=0.799-0.850) and 0.852(95% CI=0.820-0.881) for men and women, respectively, compared to other lipid ratio indicators, and the optimal cutoff values for both men and women was 0.44(p<0.001). Therefore, the AIP among the lipid ratio indicators was found to be the most useful index for diagnosing metabolic syndrome in the elderly aged 65 years or older.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.495-509
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• 2015

We study a classification problem of significant differences in the proportion of two groups known as the unbalanced classification problem. It is usually more difficult to classify classes accurately in unbalanced data than balanced data. Most observations are likely to be classified to the bigger group if we apply classification methods to the unbalanced data because it can minimize the misclassification loss. However, this smaller group is misclassified as the larger group problem that can cause a bigger loss in most real applications. We compare several classification methods for the unbalanced data using sampling techniques (up and down sampling). We also check the total loss of different classification methods when the asymmetric loss is applied to simulated and real data. We use the misclassification rate, G-mean, ROC and AUC (area under the curve) for the performance comparison.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.79-85
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• 2017

The purpose of this research was to evaluate the clinical and the instrument of convergence utility of transient elastography (FibroScan(R):electromagnetic wave) in diagnosing and treating liver ailments through a comparison and an analysis between liver function blood test and transient elastography (FibroScan(R)) in patients with chronic hepatitis B virus infection. Of all the patients with chronic hepatitis B virus infection who visited clinic B in Daejeon City between July 1, 2015, and February 28, 2016, 75 who underwent a FibroScan(R) test were selected for this study. Their laboratory and liver function test results were compared for a correlation analysis before constructing an ROC (Receiver Operation Characteristic) curve. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were 0.572 and 0.502, respectively, and showed highest correlation with fibrosis score, with statistical significance (p<0.000). Gamma glutamyltranspeptidase, total bilirubin, and alkaline phosphatase levels also showed relatively significant correlations in this order of sequence, while -fetoprotein and total protein levels did not show any statistically significant values. Albumin (-0.449) and platelet levels (-0.373) showed negative correlations with each other and no correlation with fibrosis score (p < 0.000). As liver fibrosis worsened, the accuracy of the ROC curve increased. At the F4 stage, which is the cirrhotic stage, the largest area under the curve was observed. FibroScan(R) showed significant correlation with the ALT (serum glutamic pyruvic transaminase) and AST (serum glutamic oxaloacetic transaminase) levels in the liver function test, which is a routine test for patients with chronic liver ailments. This implies that fibrosis correlates with liver inflammation severity.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.317-325
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• 2024

This study aimed to classify normal and nodule images in thyroid ultrasound images using GLCM and machine learning. The research was conducted on 600 patients who visited S Hospital in Busan and were diagnosed with thyroid nodules using thyroid ultrasound. In the thyroid ultrasound images, the ROI was set to a size of 50x50 pixels, and 21 parameters and 4 angles were used with GLCM to analyze the normal thyroid patterns and thyroid nodule patterns. The analyzed data was used to distinguish between normal and nodule diagnostic results using the SVM model and KNN model in MATLAB. As a result, the accuracy of the thyroid nodule classification rate was 94% for SVM model and 91% for the KNN model. Both models showed an accuracy of over 90%, indicating that the classification rate is excellent when using machine learning for the classification of normal thyroid and thyroid nodules. In the ROC curve, the ROC curve for the SVM model was generally higher compared to the KNN model, indicating that the SVM model has higher within-sample performance than the KNN model. Based on these results, the SVM model showed high accuracy in diagnosing thyroid nodules. This result can be used as basic data for future research as an auxiliary tool for medical diagnosis and is expected to contribute to the qualitative improvement of medical services through machine learning technology.

• Korean Journal of Psychosomatic Medicine
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• v.9 no.2
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• pp.153-163
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• 2001

This study was attempted to investigate the discriminant validity of Korean version of Eating Attitude Test-26(KEAT-26) and to provide the sensitivity, specificity and efficiency according to cutting score, which may be useful to determine the optimal cutoff point on various purposes. The KEAT-26 was administered to 108 female patients with eating disorders, 179 female participants in body slimming center, 120 female athletic college students, 227 female college students, and 183 healthy normal women. Validity was tested by ANOVA and ROC curve analysis. The results revealed that the total score of the KEAT-26 showed a statistically significance between groups and that the score of the KEAT-26 of eating disorders group was significantly higher than that of the other groups in post hoc test. In comparison of the 4 subfactor score of the KEAT-26 between groups, significant differences in main effect within groups were found in all subfactors except factor IV. ROC curve analysis showed 80% of efficiency to discriminate eating disorders group from normal control group using cutoff score on maximum discriminant efficiency and 69% of efficiency to discriminate eating disorders group from high risk groups for eating disorders. Each cutoff score on maximum in efficiency was as follows ; 25 between eating disorders group and participants in body slimming center, 19 between eating disorders group and healthy normal woman, 23 between eating disorders group and athletic college students, 21 between eating disorders group and college students. Using 22(T score 65) of the KEAT-26 as the cutoff score, sensitivity was 54%, specificity was 84%, and overall efficiency was 80%. These results indicate that the KEAT-26 has a good discriminant validity in Korean population and also suggest that the KEAT-26 may be useful assessment tool to screen the disordered eating problems on clinical and epidemiological purposes.

• Journal of the Korean Society of Radiology
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• v.11 no.4
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• pp.227-234
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• 2017

This study evaluated the usefulness of the elasticity score and strain ratio in the differential diagnosis of benign and malignant nodules in thyroid elastography. We performed a retrospective analysis based on the results of fine needle aspiration cytology. The Chi-square test and the Mann-Whitney U test were used to analyze the difference between the five degrees of elasticity score and strain ratio according to the benign and malignant thyroid nodules. ROC curve analysis was used to determine the elasticity score and the best cut-off value of the strain ratio for the prediction of malignant nodules. There was a statistically significant difference (p=0.000) between the homogeneity of the elasticity score and the difference of the strain ratio between the benign and malignant nodule groups. On the ROC curve analysis, the elasticity score and the srain ratio for predicting benign and malignant nodules were determined as AUC 0.842, 0.700, cut-off value 3, 2.49 (p=0.001). Therefore, the elasticity score and strain ratio may be useful in the differential diagnosis of thyroid nodules.

• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.201-207
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• 2019

This study has evaluated whether the method of using the combination of different risk group, according to K-TIRADS classification and K-TIRADS classification in thyroid ultrasonography is useful in a differential diagnosis of benign and malignant nodules. The subject was patients underwent thyroid ultrasonography and retrospective analysis were performed based on the results of fine needle aspiration cytology. A chi-square test was performed for the difference analysis of the score system in K-TIRADS and different risk group according to the benign and malignant of thyroid nodule. The optimized cut off value was determined by the K-TIRADS score and different risk group to predict malignant nodule through ROC curve analysis. In the differential verification result of K-TIRADS and different risk group, according to the classification of benign and malignant nodule group each showed significant difference statistically(p=.001). In the point classification according to K-TIRADS for the prediction of benign and malignant in ROC curve analysis showed AUC 0.786, Cut-off value>2(p=.001), and in the different risk group, it was decided as AUC 0.640, Cut-off value>2(p=.001). When discovering the nodule in thyroid ultrasound, it is considered that the K-TIRADAS which helps in identifying benign and malignant thyroid nodules, it is considered to be helpful in the differential diagnosis of thyroid nodules, than the classification system according to Different risk group, and when applying the classification system according to K-TIRADS, it is considered that it can reduce unnecessary fine needle aspiration cytology and could be helpful in finding the malignant nodules early.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.51-58
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• 2018

Recently, various satellite sensors have been developed and it is becoming more convenient to acquire multitemporal satellite images. Therefore, various researches are being actively carried out in the field of utilizing change detection techniques such as disaster and land monitoring using multitemporal satellite images. In particular, researches related to the development of unsupervised change detection techniques capable of extracting rapidly change regions have been conducted. However, there is a disadvantage that false detection occurs due to a spectral difference such as a seasonal change. In order to overcome the disadvantages, this study aimed to reduce the false alarm detection due to seasonal effects using the direction vector generated by applying the $S^2CVA$ (Sequential Spectral Change Vector Analysis) technique, which is one of the unsupervised change detection methods. $S^2CVA$ technique was applied to RapidEye images of the same and different seasons. We analyzed whether the change direction vector of $S^2CVA$ can remove false positives due to seasonal effects. For the quantitative evaluation, the ROC (Receiver Operating Characteristic) curve and the AUC (Area Under Curve) value were calculated for the change detection results and it was confirmed that the change detection performance was improved compared with the change detection method using only the change magnitude vector.

• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.181-196
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• 2019

Floods can be caused by a variety of factors, and the main cause of floods is the exceeding of urban drainage system or river capacity. In addition, rainfall frequently occurs that causes large watershed runoff. Since the existing methodology of preparing for flood risk map is based on hydraulic and hydrological modeling, it is difficult to analyse for a large area because it takes a long time due to the extensive data collection and complex analysis process. In order to overcome this problem, this study proposes a methodology of mapping for flood vulnerable area that considered the surface runoff mechanism. This makes it possible to reduce the time and effort required to estimate flood vulnerabilities and enable detailed analysis of large areas. The target area is Seoul, and it was confirmed that flood damage is likely to occur near selected vulnerable areas by verifying using 2×2 confusion matrix and ROC curve. By selecting and prioritizing flood vulnerable areas through the surface runoff mechanism proposed in this study, the establishment of systematic disaster prevention measures and efficient budget allocation will be possible.