• 전자공학회논문지 IE
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• v.46 no.3
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• pp.62-67
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• 2009

In this paper, ROC curves were designed by using Fuzzy Logic Systems. ROC curve is used for diagnostic evaluation and the person evaluating ROC curve is chosen as a first-level diagnostician. For rating diagnostic capability on ROC curve through learning, the chest X-ray image is used. The images used for making a diagnosis are X-ray film being both noise and signal. The result over diagnostic capability difference between the male and the female represented a man had better than a woman but that difference can be ignored.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.29-39
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• 2009

Among others, ROC and CAP curves are used to explore the discriminatory power between the defaults and non-defaults, based on the distribution of the probability of default in credit rating works. ROC and CAP curves are plotted in terms of various ratios of the probability of default. Each point on ROC and CAP curves is calculated according to cutting points (scores) for classifying between defaults and non-defaults. In this paper, adjusted ROC and CAP curves are proposed by using functions of ratios of the probability of default. It is possible to recognize the score corresponding to a point oil these adjusted curves, and we can identify the best score to show the optimal discriminatory power. Moreover, we discuss the relationships between the best score obtained from the adjusted ROC and CAP curves and the score corresponding to Kolmogorov - Smirnov statistic to test the homogeneous distribution functions of the defaults and non-defaults.

• Journal of radiological science and technology
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• v.32 no.2
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• pp.147-151
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• 2009

In general, the discrete confidence judgments that use five-step assessment method have been used to assess the medical images by ROC. TPF or FPF can be computed easily with this independent reading test. However, during experiments, it happens frequently that adequate distribution for observers is required to smoothly estimate the ROC curve. In addition, data becomes invalid for distribution of the created categories. To solve such problems or to apply the ROC interpretation to data that is not obtained from the experimental observation, the continuous confidence judgements (CCJ) has been proposed, which implements ROC interpretation using continuously-distributed experimental results without category classification has been used. As the use of CCJ to assess medical images was barely reported in Korea, we applied it to the assessment of chest digital images in this study. The results showed that a smooth ROC curve was obtained conveniently by the commercialized program and the characteristic value was measured easily. Therefore, it is recommended that this method can be applied to the assessment of digital medical images.

• 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.269-278
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• 2011

There are many researches that have considered the distribution functions and appropriate covariates corresponding to the scores in order to improve the accuracy of a diagnostic test, including the ROC curve that is represented with the relations of the sensitivity and the specificity. The ROC analysis was used by the regression model including some covariates under the assumptions that its distribution function is known or estimable. In this work, we consider a general situation that both the distribution function and the elects of covariates are unknown. For the ROC analysis, the mixtures of normal distributions are used to estimate the distribution function fitted to the credit evaluation data that is consisted of the score random variable and two sub-populations of parameters. The AUC measure is explored to compare with the nonparametric and empirical ROC curve. We conclude that the method using normal mixtures is fitted to the classical one better than other methods.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.277-286
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• 2012

For credit assessment models, the ROC curves evaluate the classification performance using two univariate cumulative distribution functions of the false positive rate and true positive rate. In this paper, it is extended to two bivariate normal distribution functions of default and non-default borrowers; in addition, the bivariate ROC curves are proposed to represent the joint cumulative distribution functions by making use of the linear function that passes though the mean vectors of two score random variables. We explore the classification performance based on these ROC curves obtained from various bivariate normal distributions, and analyze with the corresponding AUROC. The optimal threshold could be derived from the bivariate ROC curve using many well known classification criteria and it is possible to establish an optimal cut-off criteria of bivariate mixture distribution functions.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.473-483
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• 2014

Even though the ROC manifold for more than three dimensional space which is an extension of the ROC curve and surface has difficulty to represent graphically, the hypervolume under the ROC manifold (HUM) statistic can be defined and obtained based on AUC and VUS measures for the ROC curve and the ROC surface. Hence the definition and characteristics of the HUM for four dimensional space are studied in this work. By extension of the standard criterion of AUC for probabilities of default based on Basel II, the 13 classes of standard criterion of HUM are proposed in order to discriminate four classification models and some application methods are discussed. In order to explore the standard criterion of HUM whose values are obtained from various distributions, ternary plot is used and explained.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.87-101
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• 2021

Required Operational Capability(ROC), which means the performance of a weapon system, is determined when estimating the requirements of a new weapon system. It is very important to define the ROC as it has a decisive influence from acquisition of a weapon system to tactical operation. In this study, we propose a simulation methodology to verify the ROC of the Para-Observation Munition(POM), a newly developed weapon system. To this end, we propose a discrete-event simulation model that takes main performance of the weapon system constituting the ROC and environmental factors that affect performance of the weapon system as input values, and outputs operational effect as a result value. It describes various simulation logic required to implement a simulation model, and explains how to verify ROC using various simulation results such as sensitivity analysis. POM is a weapon system that does not have a similar one and that is difficult to directly utilize the military analysis model. This study can be used as a methodology to analyze the ROC and predict operational effects of weapon systems such as POM.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1489-1496
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• 2013

Consider the ROC surface which is a generalization of the ROC curve for three-class diagnostic problems. In this work, we propose ve criteria for the three-class ROC surface by extending the Youden index, the sum of sensitivity and specificity, the maximum vertical distance, the amended closest-to-(0,1) and the true rate. It may be concluded that these five criteria can be expressed as a function of two Kolmogorov-Smirnov statistics. A paired optimal thresholds could be obtained simultaneously from the ROC surface. It is found that the paired optimal thresholds selected from the ROC surface are equivalent to the two optimal thresholds found from the two ROC curves.