• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.225-238
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• 2021

The ROC, TOC, and TROC curves, which are visually descriptive methods of exploring the performance of the binary classification model, are implemented with TP, TN, FP, FN which consist of the confusion matrix, as well as their ratios TPR, TNR, FPR, FNR. In this study, we consider two types odds and then propose an odds curve representing these odds. And show the relationship between the odds curve and ROC curve. Based on the odds curve, we propose not only two statistics that measure the discriminant power of the odds curve but also the criteria for validation ratings of the odds curve. According to the shape of the odds curves, two classification distributions can be estimated and a criterion for validation ratings can be determined. The odds curve can be meaningfully used like other visual methods, and two kinds of measures for the discriminant power can be also applied together as an alternative criterion.

• Imaging Science in Dentistry
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• v.30 no.3
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• pp.155-158
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• 2000

The purpose of this paper is to explain the making procedure and the usage of receiver operating characteristic (ROC) curve for interpretation of radiographic images. The conventional radiograms obtained after the creation of the lesions in the acrylic plates and were enhanced in color. The observer were informed of which tooth to examine, the 'a priori' probability of a lesion present and the approximate diameter of the lesions. The two groups of films were interpreted separately by the same observer using the same rating scale. The following rating scale was used: A; definitely no lesion, B; probably no lesion, C; not sure, D; probably a lesion, and E; definitely a lesion. In analysis, for each observer the diagnostic results in terms of true positive (TP) and false positive (FP) decisions were plotted on a graph. The lowest point on the graph represents the TP and FP when only decisions designated as E according to the rating scale are included. The next point shows the TP and FP values when diagnoses designated as D are added and so forth. By connecting such plot points, a receiver operating characteristic (ROC) curves is obtained. The area under the curve represents the diagnostic accuracy resulting from a diagnostic performance at pure chance level and a value of 1.0 at perfect performance. This method has been known as an useful method to detect the minute difference for each radiographic technic, each observer and for the different lesion depths.

• 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.

• Journal of radiological science and technology
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• v.40 no.2
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• pp.237-243
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• 2017

This study evaluated the applicability of computer-aided diagnosis by retrospective analysis of GLCM algorithm based on cytopathological diagnosis of normal and malignant nodules in thyroid ultrasound images. In the experiment, the recognition rate and ROC curve of thyroid malignant nodule were analyzed using 6 parameters of GLCM algorithm. Experimental results showed 97% energy, 93% contrast, 92% correlation, 92% homogeneity, 100% entropy and 100% variance. Statistical analysis showed that the area under the curve of each parameter was more than 0.947 (p = 0.001) in the ROC curve, which was significant in the recognition of thyroid malignant nodules. In the GLCM, the cut-off value of each parameter can be used to predict the disease through analysis of quantitative computer-aided diagnosis.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.367-367
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• 2019

최근 기후변화로 인해 국지성 호우 및 태풍의 빈도가 빈발하고 및 규모가 커지고 있으며 그로 인한 홍수피해규모는 증가하고 있다. 본 논문에서는 도시 지역의 호우로 인한 침수유발 강우량을 산정하는 기법의 정확도를 산정하는데 목적이 있으며 이를 위해 ROC(Receiver Operation Characteristic Curve) 분석을 이용하였다. 본 논문에서는 분포형 홍수해석 모형인 S-RAT 모형과 2차원 침수해석 모형 FLO-2D을 커플링하여 호우로 인한 침수해석을 실시하였으며 강우시나리오는 설계 강우 200mm의 강우를 10% 간격으로 증가시켜 강우량 대비 침수심 자료를 모의하였다. 모의한 침수심 자료를 이용하여 유역 격자를 $1km{\times}1km$ 별 강우량-침수심 관계곡선식을 제시하였으며 개발된 곡선식을 이용하여 특정 침수심(20cm)을 유발시키는 강우량(한계강우량)을 산정하였다. 정확도 산정은 ROC(Receiver Operation Characteristic Curve) 분석 방법을 이용하여 침수 유무의 적중률에 따른 민감도와 특이도를 이용하여 AUC(Area Under the Curve)의 점수로 정확도를 판단하였다. 본 논문에서는 본 논문에서 제시한 한계강우량의 정확도를 판단하기 위하여 2011년 7월의 사당역 일대 침수사례를 이용하였다. 실제 침수정보가 없어 실제 호우사상과 실제 하수관망을 고려할 수 있는 SWMM 모형을 이용하여 침수분석을 실시하였다. 분석 결과 평균 ROC는 약 0.7로 나타났으며 5 단계의 구분에서 Fair 단계로 적정 수준의 정확도를 확보한 것으로 나타났다.

• 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.

• Journal of radiological science and technology
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• v.36 no.4
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• pp.319-326
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• 2013

The purpose of this study is to evaluate usefulness of Hybrid image and Inverse image about detection of tumor shadow in chest radiography using ROC analysis. Original images of 60 cases are selected from Standards digital image date base issued by the Japanese Society of Radiological Technology. Through computer language of C, Inverse images of 60 cases and Hybrid image of 30 cases are made. The continues reading experiment was conducted. In the case of inverse image were observed by 5 radiographer and 2 radiologist. In the case of In case of Hybrid image were observed by 3 student radiographer and 2 experienced radiographer. ROC curve are constructed using ROCKIT Program made by Metz. In Inverse image, a Az of average ROC curve was increases from 0.742 of original image to 0.775 of inverse image. In normal cases, the effect of the detrimental is same to that of the beneficial, however In abnormal cases, the beneficial effect is greater than detrimental effect. However in Hybrid image, a Az of average ROC curve was decreases from 0.5253 of original image to 0.4868 of Hybrid image. In Normal cases, the effect of the detrimental is greater than that of the Beneficial, however In abnormal cases, the Beneficial effect is greater than detrimental effect. The inverse image can be more positively considered for the detecting of tumor than the hybrid image.

• Journal of the korean veterinary medical association
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• v.36 no.2
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• pp.121-134
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• 2000

의학적 진단에서 검사결과가 연속형으로 측정되는 예는 매우 많다. 예를 들어 ELISA검사, 혈청화학적 검사, 방사선 검사 (이 경우에는 음성, 의양성, 양성등의 척도로 표현될 수 있음) 등에서는 적절한 기준을 설정한 후 이 기준점을 근거로 양성과 음성으로 판정하게 된다. 여기에서 한 가지 문제는 기준점 설정에 있다. 소위 정상 혹은 참고범위 (normal or reference range)가 분명히 있는 경우라고 실제 판정에 있어서는 질별이 없음에도 불구하고 검사결과 질병이 있는 것으로 판정할 오류 (혹은 그 반대)가 분명히 존재한다. 본 논문에서는 이러한 상황에서 접근할 수 있는 한가지 방법인 ROC 곡선에 대하여 설명하고자 한다.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.27 no.2
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• pp.7-13
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• 1997

조직학적으로 유용성이 입증된 산탈회법을 이용한 인접면 비교적 초기 치아 우식의 병소를 형성하여 진단율을 조사하였다. 산 용액을 이용하여 20개 인접면 치아우식을 20개 소구치에 형성하였고, 37개 인접면 치아우식을 30개 대구치에 형성하였다. 건전한 소구치 20개, 대구치 30개를 포함하여 총 96개 치아를 4개씩 나누어 24개의 블록을 형성하였고, 각각 2개 블록의 교합면을 교합시켜서, 교익촬영을 하였다. 촬영 결과를 36명의 치과의사들이인접면 치아우식의 유무를 기록하고, 동시에 및 ROC 분석을 위한 5 개 범주의 판독 기준으로 판독하여 기록하였다. 인접면 치아우식증 유, 무만으로 판독한 결과 진단의 sensitivity는 0.71, specificity는 0.78 이였다. ROC 분석 한 결과의 곡선도표 아래부분의 평균 면적은 약 0.806 이였다. 치아우식증 유무만으로 진단한 결과는 특정한 sensitivity와 specificity 만을 나타내지만, ROC 분석 결과는 주관적 진단 기준과 구별되는 고유의 진단 능력을 표시하는 1-specificity(False Positive)의 변화에 따른 sensitivity(True Positive)의 변화를 연속적으로 나타내어 주었다.