• The Journal of Internal Korean Medicine
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• v.31 no.4
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• pp.829-836
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• 2010

Purpose : This study investigated the diurnal rhythm of cortisol in male and female adolescents. Methods : Salivary cortisol was examined in 52 normally developing subjects aged 13 to 14 years. Subjects provided saliva samples at 08:00h, 12:00h, 16:00h and 20:00h. Results : Males and females showed similar pattern of cortisol, which elevated cortisol in the morning and decreased in the evening. There were no differences of gender at 08:00h, 12;00h and 20:00h. There were also not difference between males and females using an area under the curve analysis. Conclusions : The same diurnal cortisol rhythm were found in male and female adolescents. Further research is needed to examine differences of gender in cortisol awakening response.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.533-545
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• 2022

Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.35-47
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• 2022

The receiver operating characteristic (ROC) curve was developed to quantify the classification ability of marker values (covariates) on the response variable and has been extended to survival data with diverse missing data structure. When survival data is understood as binary data (status of being alive or dead) at each time point, the ROC curve expressed at every time point results in time-dependent ROC curve and time-dependent area under curve (AUC). In particular, a follow-up study brings the change of cohort and incomplete data structures such as censoring and competing risk. In this paper, we review time-dependent ROC estimators under several contexts and perform simulation to check the performance of each estimators. We analyzed a dementia dataset to compare the prognostic power of markers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.263-267
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• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• Current Optics and Photonics
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• v.8 no.3
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• pp.282-299
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• 2024

Target detection (TD) is a research hotspot in the field of hyperspectral imaging (HSI). Traditional TD methods often mine targets from HSIs under a single imaging condition, without considering the influence of imaging conditions. In fact, the spectra of ground objects in HSIs are uncertain and affected by the imaging conditions (weather, atmospheric, light, time, and other angle conditions including zenith angle). Hyperspectral data changes under different imaging conditions. Therefore, the detection result for a single imaging condition cannot accurately reflect the effectiveness of the detection method used. It is necessary to analyze the performance of various detection methods under different imaging conditions, to find a more applicable detection method. In this paper, we study the performance of TD methods under various land-based imaging conditions. We first summarize classical TD methods and evaluation methods. Then, the detection effects under various imaging conditions are analyzed. Finally, the concepts of the stability coefficient (SC) and effective area under the curve (EAUC) are proposed to comprehensively evaluate the applicability of detection methods under land-based imaging conditions, in terms of both detection accuracy and stability. This is conducive to our selection of detection methods with better applicability in land-based contexts, to improve detection accuracy and stability.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.30-34
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• 2007

Structural integrity of railway vehicles should last for a long period against various and continuous fatigue loadings, and the carbody structures of railway vehicle are manufactured by applying multiform welding types for each material. Since the most of cracks are occurred and proceeded at the vicinity of welding area during the lifetime of carbody structure, the fatigue strength evaluation for welding area of carbody structure should have been carried out. Rotem Company has evaluated lifetime and fatigue strength of carbody structure according to the fatigue analysis based on the international standard and/or inner-official regulation. This study introduces the fatigue analysis method that we have evaluated and calculated the damages for the welding areas of carbody structure under various fatigue loading conditions using cumulative fatigue damage rule(Miner's rule) to verify whether the cumulative damage does exceed unity. This study contains the fatigue test of specimens to derive stress-life relations(S-N curve), sub-modeling analysis and the calculation of cumulative damages under fatigue loading. The fatigue analysis verifies the welding area shall be capable of withstanding under fatigue loading, identifies how critical area shall be selected and presents the principles to be used for design verification.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.541-553
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• 2020

The receiver operating characteristic (ROC) curve is expressed as both sensitivity and specificity; in addition, some optimal thresholds using the ROC curve are also represented with both sensitivity and specificity. In addition to the sensitivity and specificity, the expected usefulness function is considered as disease prevalence and usefulness. In particular, partial the area under the ROC curve (AUC) on a certain range should be compared when the AUCs of the crossing ROC curves have similar values. In this study, partial AUCs representing high sensitivity and specificity are proposed by using sensitivity and specificity lines, respectively. Assume various distribution functions with ROC curves that are crossing and AUCs that have the same value. We propose a method to improve the discriminant power of the classification models while comparing the partial AUCs obtained using sensitivity and specificity lines.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• The Korean Journal of Pain
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• v.33 no.1
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• pp.54-59
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• 2020

Background: The median nerve cross-sectional area (MNCSA) is a useful morphological parameter for the evaluation of carpal tunnel syndrome (CTS). However, there have been limited studies investigating the anatomical basis of median nerve flattening. Thus, to evaluate the connection between median nerve flattening and CTS, we carried out a measurement of the median nerve thickness (MNT). Methods: Both MNCSA and MNT measurement tools were collected from 20 patients with CTS, and from 20 control individuals who underwent carpal tunnel magnetic resonance imaging (CTMRI). We measured the MNCSA and MNT at the level of the hook of hamate on CTMRI. The MNCSA was measured on the transverse angled sections through the whole area. The MNT was measured based on the most compressed MNT. Results: The mean MNCSA was 9.01 ± 1.94 ㎟ in the control group and 6.58 ± 1.75 ㎟ in the CTS group. The mean MNT was 2.18 ± 0.39 mm in the control group and 1.43 ± 0.28 mm in the CTS group. Receiver operating characteristics curve analysis demonstrated that the optimal cut-off value for the MNCSA was 7.72 ㎟, with 75.0% sensitivity, 75.0% specificity, and an area under the curve (AUC) of 0.82 (95% confidence interval [CI], 0.69-0.95). The best cut off-threshold of the MNT was 1.76 mm, with 85% sensitivity, 85% specificity, and an AUC of 0.94 (95% CI, 0.87-1.00). Conclusions: Even though both MNCSA and MNT were significantly associated with CTS, MNT was identified as a more suitable measurement parameter.