• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.911-920
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• 2004

We try receiver operating characteristic(ROC) curves by neural networks of logistic function. The models are shown to arise from model classification for normal (diseased) and abnormal (nondiseased) groups in medical research. A few goodness-of-fit test statistics using normality curves are discussed and the performances using neural networks of logistic function are conducted.

• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.97-101
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• 2016

In the field of clinical medicine, diagnostic accuracy studies refer to the degree of agreement between the index test and the reference standard for the discriminatory ability to identify a target disorder of interest in a patient. The receiver operating characteristic (ROC) curve offers a graphical display the trade-off between sensitivity and specificity at each cutoff for a diagnostic test and is useful in assigning the best cutoff for clinical use. In this end, the ROC curve analysis is a useful tool for estimating and comparing the accuracy of competing diagnostic tests. This paper reviews briefly the measures of diagnostic accuracy such as sensitivity, specificity, and area under the ROC curve (AUC) that is a summary measure for diagnostic accuracy across the spectrum of test results. In addition, the methods of creating an ROC curve in single diagnostic test with five-category discrete scale for disease classification from healthy individuals, meaningful interpretation of the AUC, and the applications of ROC methodology in clinical medicine to determine the optimal cutoff values have been discussed using a hypothetical example as an illustration.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.79-89
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• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.195-197
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• 2001

Data Mining is used to discover patterns and relationships in huge amounts of data. Researchers in many different fields have shown great interest in data mining analysis. Using the classification technique of data mining analysis, the available model for Receiver Operating Characteristic(ROC) method is presented. We present that this may help analyze result of data mining techniques.

• Bulletin of the Korean Mathematical Society
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• v.48 no.3
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• pp.523-537
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• 2011

A receiver operating characteristic (ROC) curve plots the true positive rate of a classier against its false positive rate, both of which are accuracy measures of the classier. The ROC curve has several interesting geometrical properties, including concavity which is a necessary condition for a classier to be optimal. In this paper, we study the nonparametric maximum likelihood estimator (NPMLE) of a concave ROC curve and its modification to reduce bias. We characterize the NPMLE as a solution to a geometric programming, a special type of a mathematical optimization problem. We find that the NPMLE is close to the convex hull of the empirical ROC curve and, thus, has smaller variance but positive bias at a given false positive rate. To reduce the bias, we propose a modification of the NPMLE which minimizes the $L_1$ distance from the empirical ROC curve. We numerically compare the finite sample performance of three estimators, the empirical ROC curve, the NMPLE, and the modified NPMLE. Finally, we apply the estimators to estimating the optimal ROC curve of the variance-threshold classier to segment a low depth of field image and to finding a diagnostic tool with multiple tests for detection of hemophilia A carrier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.910-924
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• 2010

As Internet usage has increased, the risk of adolescents being exposed to adult content and harmful information on the Internet has also risen. To help prevent adolescents accessing this content, a novel detection method for adult images is proposed. The proposed method involves three steps. First, the Image Of Interest (IOI) is extracted from the image background. Second, the IOI is distinguished from the segmented image using a novel weighting mask, and it is determined to be acceptable or unacceptable. Finally, the features (color and texture) of the IOI or original image are compared to a critical value; if they exceed that value then the image is deemed to be an adult image. A Receiver Operating Characteristic (ROC) curve analysis was performed to define this optimal critical value. And, the textural features are identified using a gray level co-occurrence matrix. The proposed method increased the precision level of detection by applying a novel weighting mask and a receiver operating characteristic curve. To demonstrate the effectiveness of the proposed method, 2850 adult and non-adult images were used for experimentation.

• 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.

• Journal of Veterinary Clinics
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• v.19 no.3
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• pp.312-315
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• 2002

Diagnostic tests often require the determination of cut-off values that discriminate uninfected from infected individuals. The receiver operating characteristic (ROC) curve has been frequently used to attain this purpose and gives a representation of diagnostic accuracy (sensitivity and specificity) of a prediction model when varying the cut-point of a decision rule on a whole spectrum. We have written and tested a visual basic application program in EXCEL for maximum likelihood estimation of a binormal ROC curve, which also computes univariate statistics of a diagnostic test employed. Examples applying for computed tomographic images in radiology and methicillin-resistant Staphylococcus aureus research are given to illustrate this approach. This stand-alone module is available from the first author on request.

• 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 Contents
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• v.10 no.1
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• pp.23-28
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• 2014

When developing a classifier using various objective functions, it is important to compare the performances of the classifiers. Although there are statistical analyses of objective functions for classifiers, simulation results can provide us with direct comparison results and in this case, a comparison criterion is considerably critical. A Receiver Operating Characteristics (ROC) graph is a simulation technique for comparing classifiers and selecting a better one based on a performance. In this paper, we adopt the ROC graph to compare classifiers trained by mean-squared error, cross-entropy error, classification figure of merit, and the n-th order extension of cross-entropy error functions. After the training of feed-forward neural networks using the CEDAR database, the ROC graphs are plotted to help us identify which objective function is better.