• 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.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.377-387
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• 2015

The area under the ROC curve can be represented by both Mann-Whitney and Wilcoxon rank sum statistics. Consider an ROC surface and manifold equal to three dimensions or more. This paper finds that the volume under the ROC surface (VUS) and the hypervolume under the ROC manifold (HUM) could be derived as functions of both conditional Mann-Whitney statistics and conditional Wilcoxon rank sum statistics. The nullhypothesis equal to three distribution functions or more are identical can be tested using VUS and HUM statistics based on the asymptotic large sample theory of Wilcoxon rank sum statistics. Illustrative examples with three and four random samples show that two approaches give the same VUS and $HUM^4$. The equivalence of several distribution functions is also tested with VUS and $HUM^4$ in terms of conditional Wilcoxon rank sum statistics.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.911-921
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• 2009

Receiver Operating Characteristic(ROC) and Cumulative Accuracy Profile(CAP) curves are two methods used to assess the discriminatory power of different credit-rating approaches. The points of optimal classification accuracy on an ROC curve and of maximal profit on a CAP curve can be found by using iso-performance tangent lines, which are based on the standard notion of accuracy. In this paper, we offer an alternative accuracy measure called the true rate. Using this rate, one can obtain alternative optimal threshold points on both ROC and CAP curves. For most real populations of borrowers, the number of the defaults is much less than that of the non-defaults, and in such cases the true rate may be more efficient than the accuracy rate in terms of cost functions. Moreover, it is shown that both alternative scores of optimal classification accuracy and maximal profit are the identical, and this single score coincides with the score corresponding to Kolmogorov-Smirnov statistic used to test the homogeneous distribution functions of the defaults and non-defaults.

• 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.

• 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.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.829-839
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• 2013

This study is a descriptive research study with the purpose of predicting and comparing factors of depression affecting residents in a metropolitan city by using logistic regression analysis and decision-making tree analysis. The subjects for the study were 462 residents ($20{\leq}aged{\angle}65$) in a metropolitan city. This study collected data between October 7, 2011 and October 21, 2011 and analyzed them with frequency analysis, percentage, the mean and standard deviation, ${\chi}^2$-test, t-test, logistic regression analysis, roc curve, and a decision-making tree by using SPSS 18.0 program. The common predicting variables of depression in community residents were social dysfunction, perceived physical symptom, and family support. The specialty and sensitivity of logistic regression explained 93.8% and 42.5%. The receiver operating characteristic (roc) curve was used to determine an optimal model. The AUC (area under the curve) was .84. Roc curve was found to be statistically significant (p=<.001). The specialty and sensitivity of decision-making tree analysis were 98.3% and 20.8% respectively. As for the whole classification accuracy, the logistic regression explained 82.0% and the decision making tree analysis explained 80.5%. From the results of this study, it is believed that the sensitivity, the classification accuracy, and the logistics regression analysis as shown in a higher degree may be useful materials to establish a depression prediction model for the community residents.

• The korean journal of orthodontics
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• v.25 no.2 s.49
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• pp.153-163
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• 1995

In this study, Receiver Operating Characteristic(ROC) analysis was used to evaluate the ability of cephalometric measurements to identify patients with Class III malocclusions. ROC analysis is the method for determining the validity of a diagnostic measure and for evaluating the relative value of diagnostic tests. The sample consisted of 496 patients with malocclusion. Class III malocclusion is defined as the dental relationship for which The mesiobuccal groove of the lower first molar is deviated mesially from the mesiobuccal cusp of the upper first molar. Of the total sample of 496 patients, 245 had Class III malocclusions. 16 cephalometric measurements were selected, each of which was treated as a diagnostic test. The ROC curves were generated for each cephalometric measurement with intervals of $1.0^{\circ}$ for angular measurements, 1.0mm for linear measurements. The area under the ROC curves was measured for direct comparison among different diagnostic tests. The results were as follows; 1. The 'Wits' appraisal was found to be a better diagnostic criterion for the presence of Class III malocclusion than any other commonly'used cephalometric measurement. 2. AB plane angle, ANB angle, App-Bpp distance, AF-BF distance, APDI, Distance of point A and Pog to N perpendicular, maxillomandibular differential had high diagnostic value. 3. Cephalometric measurements which evaluate the position of the mandible had moderate diagnostic value. 4. Cephalometric measurements related to the maxilla discriminated least between patients with and without Class III malocclusion.

• Imaging Science in Dentistry
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• v.34 no.1
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• pp.1-5
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• 2004

Purpose: To evaluate the diagnostic accuracy of occlusal and proximal caries detection using CCD, CMOS, PSP and film system. Materials and Methods : 32 occlusal and 30 proximal tooth surfaces were radiographed under standardized conditions using 3 digital systems; CCD (CDX-2000HQ, Biomedysis Co., Seoul, Korea), CMOS (Schick, Schick Inc., Long Island, USA), PSP (Digora/sup (R)/FMX, Orion Co./Soredex, Helsinki, Finland) and I film system (Kodak Insight, Eastman Kodak, Rochester, USA). 5 observers examined the radiographs for occlusal and proximal caries using a 5-point confidence scale. The presence of caries was validated histologically and radiographically. Diagnostic accuracy was evaluated using ROC curve areas (Az). Results: Analysis using ROC curves revealed the area under each curve which indicated a diagnostic accuracy. For occlusal caries, Kodak Insight film had an Az of 0.765, CCD one of 0.730, CMOS one of 0.742 and PSP one of 0.735. For proximal caries, Kodak Insight film had an Az of 0.833, CCD one of 0.832, CMOS one of 0.828 and PSP one of 0.868. No statistically significant difference was noted between any of the imaging modalities. Conclusion: CCD, CMOS, PSP and film performed equally well in the detection of occlusal and proximal dental caries. CCD, CMOS and PSP-based digital images provided a level of diagnostic performance comparable to Kodak Insight film.

• Journal of Korean Academy of Nursing
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• v.35 no.1
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• pp.47-55
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• 2005

Purpose: This study was to evaluate the validity of the Pediatric Index of Mortality Ⅱ(PIM Ⅱ). Method: The first values on PIM Ⅱ variables following ICU admission were collected from the patient's charts of 548 admissions retrospectively in three ICUs(medical, surgical, and neurosurgical) at P University Hospital and a cardiac ICU at D University Hospital in Busan from January 1, 2002 to December 31, 2003. Data was analyzed with the SPSSWIN 10.0 program for the descriptive statistics, correlation coefficient, standardized mortality ratio(SMR), validity index(sensitivity, specificity, positive predictive value, negative predictive value), and AUC of ROC curve. Result: The mortality rate was 10.9% (60 cases) and the predicted death rate was 9.5%. The correlation coefficient(r) between observed and expected death rates was .929(p<.01) and SMR was 1.15. Se, Sp, pPv, nPv, and the correct classification rate were .80, .96, .70, .98, and 94.0% respectively. In addition, areas under the curve (AUC) of the receiver operating characteristic(ROC) was 0.954 (95% CI=0.919～0.989). According to demographic characteristics, mortality was underestimated in the medical group and overestimated in the surgical group. In addition, the AUCs of ROC curve were generally high in all subgroups. Conclusion: The PIM Ⅱ showed a good, so it can be utilized for the subject hospital. better.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1367-1375
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• 2015

Consider the linear growth models for longitudinal data analysis. Several kind of linear growth models are selected such as time-effect and random-effect models as well as a dummy variable included model. In this work, simulation data are generated with normality assumption, and both binormal ROC curve and AUC are obtained and compared for various linear growth models. It is found that ROC curves have different shapes and AUC increase slowly, as values of the covariance increase and the time passes for random-effect models. On the other hand, AUC increases very fast as values of covariance decrease. When the covariance has positive value, we explored that the variances of random-effect models increase and the increment of AUC is smaller than that of AUC for time-effect models. And the increment of AUC for time-effect models is larger than the increment for random-effect models.