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Journal articles on the topic 'Receiver Operating Characteristic (ROC) Curve'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Yang, Shengping, and Gilbert Berdine. "The receiver operating characteristic (ROC) curve." Southwest Respiratory and Critical Care Chronicles 5, no. 19 (May 5, 2017): 34. http://dx.doi.org/10.12746/swrccc.v5i19.391.

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2

Fan, Jerome, Suneel Upadhye, and Andrew Worster. "Understanding receiver operating characteristic (ROC) curves." CJEM 8, no. 01 (January 2006): 19–20. http://dx.doi.org/10.1017/s1481803500013336.

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In this issue of the Journal, Auer and colleagues conclude that serum levels of neuron-specific enolase (NSE), a biochemical marker of ischemic brain injury, may have clinical utility for the prediction of survival to hospital discharge in patients experiencing the return of spontaneous circulation following at least 5 minutes of cardiopulmonary resuscitation. The authors used a receiver operating characteristic (ROC) curve to illustrate and evaluate the diagnostic (prognostic) performance of NSE. We explain ROC curve analysis in the following paragraphs.

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Nahm, Francis Sahngun. "Receiver operating characteristic curve: overview and practical use for clinicians." Korean Journal of Anesthesiology 75, no. 1 (February 1, 2022): 25–36. http://dx.doi.org/10.4097/kja.21209.

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Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametric/nonparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.

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Kumar, Rajeev, and Abhaya Indrayan. "Receiver operating characteristic (ROC) curve for medical researchers." Indian Pediatrics 48, no. 4 (April 2011): 277–87. http://dx.doi.org/10.1007/s13312-011-0055-4.

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5

Park, Seong Ho, Jin Mo Goo, and Chan-Hee Jo. "Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists." Korean Journal of Radiology 5, no. 1 (2004): 11. http://dx.doi.org/10.3348/kjr.2004.5.1.11.

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6

Kawada, Tomoyuki. "Sample Size in Receiver-Operating Characteristic (ROC) Curve Analysis." Circulation Journal 76, no. 3 (2012): 768. http://dx.doi.org/10.1253/circj.cj-11-1408.

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Yoshinaga, Keiichiro, Kazumasa Tsukamoto, and Nagara Tamaki. "Sample Size in Receiver-Operating Characteristic (ROC) Curve Analysis." Circulation Journal 76, no. 3 (2012): 769. http://dx.doi.org/10.1253/circj.cj-11-1503.

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Cook, Jonathan A., and Ashish Rajbhandari. "Heckroccurve: ROC Curves for Selected Samples." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (March 2018): 174–83. http://dx.doi.org/10.1177/1536867x1801800110.

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Receiver operating characteristic (ROC) curves can be misleading when they are constructed with selected samples. In this article, we describe heckroccurve, which implements a recently developed procedure for plotting ROC curves with selected samples. The command estimates the area under the ROC curve and a graphical display of the curve. A variety of plot options are available, including the ability to add confidence bands to the plot.

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9

Hays, Ron D. "ROC: Estimation of the Area Under a Receiver Operating Characteristic Curve." Applied Psychological Measurement 14, no. 2 (June 1990): 208. http://dx.doi.org/10.1177/014662169001400209.

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Morrison, Ann Michelle, Kelly Coughlin, James P. Shine, Brent A. Coull, and Andrea C. Rex. "Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables." Applied and Environmental Microbiology 69, no. 11 (November 2003): 6405–11. http://dx.doi.org/10.1128/aem.69.11.6405-6411.2003.

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ABSTRACT Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability to correctly classify water as suitable or unsuitable for swimming at a maximum threshold Enterococcus density of 104 CFU/100 ml. Sensitivity and specificity values were determined for each unique previous day's Enterococcus density and antecedent rainfall volume and used to construct ROC curves. The area under the ROC curve was used to compare the accuracies of the indicator variables. Twenty-four-hour antecedent rainfall classified elevated Enterococcus densities more accurately than previous day's Enterococcus density (P = 0.079). An empirically derived threshold for 48-h antecedent rainfall, corresponding to a sensitivity of 0.75, was determined from the 1996 to 2000 data and evaluated to ascertain if the threshold would produce a 0.75 sensitivity with independent water quality data collected in 2001 from the same beaches.

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11

Grzybowski, Mary, and John G. Younger. "Statistical Methodology: III. Receiver Operating Characteristic (ROC) Curves." Academic Emergency Medicine 4, no. 8 (August 1997): 818–26. http://dx.doi.org/10.1111/j.1553-2712.1997.tb03793.x.

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12

Tripepi, Giovanni, Kitty J. Jager, Friedo W. Dekker, and Carmine Zoccali. "Diagnostic methods 2: receiver operating characteristic (ROC) curves." Kidney International 76, no. 3 (August 2009): 252–56. http://dx.doi.org/10.1038/ki.2009.171.

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13

Han, Hyunsuk. "The Utility of Receiver Operating Characteristic Curve in Educational Assessment: Performance Prediction." Mathematics 10, no. 9 (April 30, 2022): 1493. http://dx.doi.org/10.3390/math10091493.

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When examinees are classified into groups based on scores from educational assessment, two indices are widely used to gauge the psychometric quality of the classifications: accuracy and consistency. The two indices take correct classifications into consideration while overlooking incorrect ones, where unbalanced class distribution threatens the validity of results from the accuracy and consistency indices. The single values produced from the two indices also fail to address the inconsistent accuracy of the classifier across different cut score locations. The current study proposed the concept of classification quality, which utilizes the receiver operating characteristics (ROC) graph to comprehensively evaluate the performance of classifiers. The ROC graph illustrates the tradeoff between benefits (true positive rate) and costs (false positive rate) in classification. In this article, a simulation study was conducted to demonstrate how to generate and interpret ROC graphs in educational assessment and the benefits of using ROC graphs to interpret classification quality. The results show that ROC graphs provide an efficient approach to (a) visualize the fluctuating performance of scoring classifiers, (b) address the unbalanced class distribution issue inherent in the accuracy and consistency indices, and (c) produce more accurate estimation of the classification results.

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14

Shultz, E. K. "Multivariate receiver-operating characteristic curve analysis: prostate cancer screening as an example." Clinical Chemistry 41, no. 8 (August 1, 1995): 1248–55. http://dx.doi.org/10.1093/clinchem/41.8.1248.

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Abstract The evolution of test performance analysis should include the long-term costs and benefits associated with testing. Evolutionary laboratory techniques to achieve this include introduction of a new methodological technique, a multivariate extension to a current analytical technique, receiver-operating characteristic (ROC) curve analysis (MultiROC analysis). This extension to ROC methodology allows the comparison of composite test rules in a format similar to that of ROC curves. Statistical properties, guidelines for use, and a detailed example are described. MultiROC is used in the outcomes analysis of the value of screening for prostate cancer. The effect of age and different test decision thresholds are examined in an extension of a previously published outcomes analysis. The results indicate that the variations in test performances caused by these components are important in assigning a final cost:benefit ratio of screening for prostate cancer.

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Hughes, Gareth. "On the Binormal Predictive Receiver Operating Characteristic Curve for the Joint Assessment of Positive and Negative Predictive Values." Entropy 22, no. 6 (May 26, 2020): 593. http://dx.doi.org/10.3390/e22060593.

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The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC curve in the context of other methods used in statistical evaluation of probabilistic disease forecasts based on the analysis of predictive values; in particular, the index of separation (PSEP) and the leaf plot. An information theoretic perspective on evaluation is also outlined. Five straightforward recommendations are made with a view to aiding understanding and interpretation of the sometimes-complex patterns generated by PROC curve analysis. The PROC curve and related analyses augment the perspective provided by traditional ROC curve analysis. Here, the binormal ROC model provides the exemplar for investigation of the PROC curve, but potential application extends to analysis based on other distributional models as well as to empirical analysis.

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Irwin, R. John, and Michael J. Hautus. "Lognormal Lorenz and normal receiver operating characteristic curves as mirror images." Royal Society Open Science 2, no. 2 (February 2015): 140280. http://dx.doi.org/10.1098/rsos.140280.

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The Lorenz curve for assessing economic inequality depicts the relation between two cumulative distribution functions (CDFs), one for the distribution of incomes or wealth and the other for their first-moment distribution. By contrast, the receiver operating characteristic (ROC) curve for evaluating diagnostic systems depicts the relation between the complements of two CDFs, one for the distribution noise and the other for the distribution of signal plus noise. We demonstrate that the lognormal model of the Lorenz curve, which is often adopted to model the distribution of income and wealth, is a mirror image of the equal-variance normal model of the ROC curve, which is a fundamental model for evaluating diagnostic systems. The relationship between these two models extends the potential application of each. For example, the lognormal Lorenz curve can be used to evaluate diagnostic systems derived from equal-variance normal distributions.

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17

Takenouchi, Takashi, Osamu Komori, and Shinto Eguchi. "An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification." Neural Computation 24, no. 10 (October 2012): 2789–824. http://dx.doi.org/10.1162/neco_a_00336.

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While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.

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18

Vardhan, R. Vishnu, and S. Balaswamy. "Improved Methods for Estimating Areas under the Receiver Operating Characteristic Curves." International Journal of Green Computing 4, no. 2 (July 2013): 58–75. http://dx.doi.org/10.4018/jgc.2013070105.

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ROC Curve is the most widely used statistical technique for classifying an individual into one of the two pre-determined groups basing on test result. Area under the curve (AUC) is a measure of accuracy which exhibits the discriminating power of the test with respect to a threshold or cutoff value. In medical diagnosis, this technique has its relevance to study and compare different diagnostic tests. In this paper, a method is proposed to estimate the AUC of Binormal ROC model by taking into account the confidence interval of mean and corresponding variances.

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19

Chance, Frances S. "Receiver Operating Characteristic (ROC) Analysis for Characterizing Synaptic Efficacy." Journal of Neurophysiology 97, no. 2 (February 2007): 1799–808. http://dx.doi.org/10.1152/jn.00885.2006.

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The role of background synaptic activity in cortical processing has recently received much attention. How do individual neurons extract information when embedded in a noisy background? When examining the impact of a synaptic input on postsynaptic firing, it is important to distinguish a change in overall firing probability from a true change in neuronal sensitivity to a particular input (synaptic efficacy) that corresponds to a change in detection performance. Here we study the impact of background synaptic input on neuronal sensitivity to individual synaptic inputs using receiver operating characteristic (ROC) analysis. We use the area under the ROC curve as a measure of synaptic efficacy, here defined as the ability of a postsynaptic action potential to identify a particular synaptic input event. An advantage of using ROC analysis to measure synaptic efficacy is that it provides a measure that is independent of postsynaptic firing rate. Furthermore, changes in mean excitation or inhibition, although affecting overall firing probability, do not modulate synaptic efficacy when measured in this way. Changes in overall conductance also affect firing probability but not this form of synaptic efficacy. Input noise, here defined as the variance of the input current, does modulate synaptic efficacy, however. This effect persists when the change in input variance is coupled with a change in conductance (as would result from changing background activity).

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Karun, Kalesh M., and Amitha Puranik. "RECEIVER OPERATING CHARACTERISTIC CURVE ANALYSIS IN DIAGNOSTIC RESEARCH: A REVIEW." International Journal of Research in Ayurveda and Pharmacy 13, no. 3 (June 7, 2022): 132–33. http://dx.doi.org/10.7897/2277-4343.130374.

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Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A receiver operating characteristic curve is a graphical technique used to identify the optimal cut-off point for a continuous variable. Implementation of ROC analysis is currently possible using various statistical software packages. However, the process is straightforward in the EZR package of R software. This present study aims to provide a tutorial using a simple example and a detailed description of the procedure in EZR software. The information provided can help the researchers perform the analysis independently.

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21

Song, Sang Wook. "Using the Receiver Operating Characteristic (ROC) Curve to Measure Sensitivity and Specificity." Korean Journal of Family Medicine 30, no. 11 (2009): 841. http://dx.doi.org/10.4082/kjfm.2009.30.11.841.

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22

Campbell, Gregory, and Makarand V. Ratnaparkhi. "An application of lomax distributions in receiver operating characteristic(roc)curve analysis." Communications in Statistics - Theory and Methods 22, no. 6 (January 1993): 1681–87. http://dx.doi.org/10.1080/03610929308831110.

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23

Sittig, Dean F., Jeffrey I. Clyman, Kei H. Cheung, and Perry L. Miller. "A456 RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE HELPS OPTIMIZE BLOOD PRESSURE DETECTION ALGORITHMS." Anesthesiology 73, no. 3A (September 1, 1990): NA. http://dx.doi.org/10.1097/00000542-199009001-00454.

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24

Moumena, Ahmed. "Anomalies Detection Based on the ROC Analysis using Classifiers in Tactical Cognitive Radio Systems: A survey." IAES International Journal of Artificial Intelligence (IJ-AI) 5, no. 3 (September 1, 2016): 105. http://dx.doi.org/10.11591/ijai.v5.i3.pp105-116.

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Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis based on the anomaly detection using classifiers for using them in research. In recent years have been increasingly adopted in the machine learning and data mining research communities. This survey gives definitions of the anomaly detection theory and how to use one ROC curve, what a ROC curve, when we use ROC curves.

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Chen, Yufei, Cheng Zhang, Xianhui Liu, and Gang Wang. "Focal Liver Lesion Classification Based on Receiver Operating Characteristic Analysis." Journal of Medical Imaging and Health Informatics 9, no. 2 (February 1, 2019): 284–92. http://dx.doi.org/10.1166/jmihi.2019.2609.

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Background: Computer-Aided Diagnosis (CAD) on Focal Liver Lesion (FLL) has been widely researched. It aims at classifying liver images into malignant or benign, so as to help doctors to make corresponding diagnosis. In most existing CAD systems, the automatic decision strategies on challenging cases usually lead to risky diagnosis. Objective: In this paper, we adopted a ROC optimal abstention model for FLL classification to reduce the misclassification risk. Method: The workflow of ROC based FLL classification includes the stages of feature extraction, statistic for building ROC curve and ROC optimal abstaining classification. Through investigating the properties of ROC, we can automatically find two optimal thresholds for building the abstention model. A part of cases refrains from being classified to achieve the lowest misclassification cost. Results: The model classifies the FLL medical records into positive (malignant), negative (benign) and abstaining cases. The abstained challenging cases can be carefully examined by experts in order to reduce the misclassification risk. Conclusion: Abundant experiments indicate that the proposed method can achieve satisfied results and is effective for FLL diagnosis.

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Moise, Alain, Bernard Clement, and Marios Raissis. "A test for crossing receiver operating characteristic (roc) curves." Communications in Statistics - Theory and Methods 17, no. 6 (January 1988): 1985–2003. http://dx.doi.org/10.1080/03610928808829727.

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27

Peng, Liang, and Xiao-Hua Zhou. "Local linear smoothing of receiver operating characteristic (ROC) curves." Journal of Statistical Planning and Inference 118, no. 1-2 (January 2004): 129–43. http://dx.doi.org/10.1016/s0378-3758(02)00394-4.

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Hughes, Gareth, Jennifer Kopetzky, and Neil McRoberts. "Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis." Entropy 22, no. 9 (August 26, 2020): 938. http://dx.doi.org/10.3390/e22090938.

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The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and specificity) and prevalence, and as a function of predictive values and prevalence. Mutual information calculated from a 2 × 2 prediction-realization table for a specified risk score threshold on an ROC curve is the same as the mutual information calculated at the same risk score threshold on a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold that maximizes mutual information is the same on both the ROC curve and the corresponding PROC curve. Phytopathologists and clinicians who have previously relied solely on ROC curve summary statistics when formulating risk thresholds for application in practical agricultural or clinical decision-making contexts are thus presented with a methodology that brings predictive values within the scope of that formulation.

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Jang, Eun Jin, and Dal Ho Kim. "Bayesian model for the receiver operating characteristic curve using the skew normal distribution." Journal of the Korean Data And Information Science Society 32, no. 1 (January 31, 2021): 15–24. http://dx.doi.org/10.7465/jkdi.2021.32.1.15.

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Valadares, Agnes Araujo, Paulo Schiavom Duarte, Giovanna Carvalho, Carla Rachel Ono, George Barberio Coura-Filho, Heitor Naoki Sado, Marcelo Tatit Sapienza, and Carlos Alberto Buchpiguel. "Receiver operating characteristic (ROC) curve for classification of18F-NaF uptake on PET/CT." Radiologia Brasileira 49, no. 1 (February 2016): 12–16. http://dx.doi.org/10.1590/0100-3984.2014.0119.

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Abstract Objective: To assess the cutoff values established by ROC curves to classify18F-NaF uptake as normal or malignant. Materials and Methods: PET/CT images were acquired 1 hour after administration of 185 MBq of18F-NaF. Volumes of interest (VOIs) were drawn on three regions of the skeleton as follows: proximal right humerus diaphysis (HD), proximal right femoral diaphysis (FD) and first vertebral body (VB1), in a total of 254 patients, totalling 762 VOIs. The uptake in the VOIs was classified as normal or malignant on the basis of the radiopharmaceutical distribution pattern and of the CT images. A total of 675 volumes were classified as normal and 52 were classified as malignant. Thirty-five VOIs classified as indeterminate or nonmalignant lesions were excluded from analysis. The standardized uptake value (SUV) measured on the VOIs were plotted on an ROC curve for each one of the three regions. The area under the ROC (AUC) as well as the best cutoff SUVs to classify the VOIs were calculated. The best cutoff values were established as the ones with higher result of the sum of sensitivity and specificity. Results: The AUCs were 0.933, 0.889 and 0.975 for UD, FD and VB1, respectively. The best SUV cutoffs were 9.0 (sensitivity: 73%; specificity: 99%), 8.4 (sensitivity: 79%; specificity: 94%) and 21.0 (sensitivity: 93%; specificity: 95%) for UD, FD and VB1, respectively. Conclusion: The best cutoff value varies according to bone region of analysis and it is not possible to establish one value for the whole body.

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Park, Sun-Il, and Tae-Ho O. "Application of Receiver Operating Characteristic (ROC) Curve for Evaluation of Diagnostic Test Performance." Journal of Veterinary Clinics 33, no. 2 (April 30, 2016): 97. http://dx.doi.org/10.17555/jvc.2016.04.33.2.97.

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Lu, Qing, Nancy Obuchowski, Sungho Won, Xiaofeng Zhu, and Robert C. Elston. "Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests." Biometrics 66, no. 2 (June 8, 2009): 586–93. http://dx.doi.org/10.1111/j.1541-0420.2009.01278.x.

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Kay, Elizabeth Jane, and Robin Knill-Jones. "Variation in restorative treatment decisions: application of Receiver Operating Characteristic curve (ROC) analysis." Community Dentistry and Oral Epidemiology 20, no. 3 (June 1992): 113–17. http://dx.doi.org/10.1111/j.1600-0528.1992.tb01542.x.

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Shidham, Vinod, Dilip Gupta, Lorenzo M. Galindo, Marian Haber, Carolyn Grotkowski, Pamela Edmonds, Steven J. Subichin, Varghese George, and James England. "Intraoperative scrape cytology: Comparison with frozen sections, using receiver operating characteristic (ROC) curve." Diagnostic Cytopathology 23, no. 2 (2000): 134–39. http://dx.doi.org/10.1002/1097-0339(200008)23:2<134::aid-dc14>3.0.co;2-n.

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Wandishin, Matthew S., and Steven J. Mullen. "Multiclass ROC Analysis." Weather and Forecasting 24, no. 2 (April 1, 2009): 530–47. http://dx.doi.org/10.1175/2008waf2222119.1.

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Abstract Receiver operating characteristic (ROC) curves have become a common analysis tool for evaluating forecast discrimination: the ability of a forecast system to distinguish between events and nonevents. As is implicit in that statement, application of the ROC curve is limited to forecasts involving only two possible outcomes, such as rain and no rain. However, many forecast scenarios exist for which there are multiple possible outcomes, such as rain, snow, and freezing rain. An extension of the ROC curve to multiclass forecast problems is explored. The full extension involves high-dimensional hypersurfaces that cannot be visualized and that present other problems. Therefore, several different approximations to the full extension are introduced using both artificial and actual forecast datasets. These approximations range from sets of simple two-class ROC curves to sets of three-dimensional ROC surfaces. No single approximation is superior for all forecast problems; thus, the specific aims in evaluating the forecast must be considered.

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Lavazza, Luigi, and Sandro Morasca. "Considerations on the region of interest in the ROC space." Statistical Methods in Medical Research 31, no. 3 (December 20, 2021): 419–37. http://dx.doi.org/10.1177/09622802211060515.

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Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

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Janičić, Bojan, and Zdenka Novović. "Procena uspešnosti u klasifikovanju rezultata na osnovu graničnih (cut-off) skorova: Receiver operating characteristic curve." Primenjena psihologija 4, no. 4 (December 23, 2011): 335–51. http://dx.doi.org/10.19090/pp.2011.4.335-351.

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Cilj rada je da se ukaže na mogućnosti upotrebe ROC krive (engl. receiver operating characteristic curve) za utvrđivanje klasifikatornih mogućnosti testa. Objašnjeni su pojmovi senzitivnosti i specifičnosti koje leže u osnovi izrade ROC krive, a data su tumačenja i formule i za izračunavanje pozitivne i negativne prediktivne vrednosti, kao i tačnosti testa. ROC kriva je grafički prikaz senzitivnosti i specifičnosti za svaki mogući granični skor (rezultat na testu) u koordinatnom sistemu gde su na ordinati prikazane vrednosti senzitivnosti, a na apscisi vrednosti specifičnosti oduzete od 1. Objašnjeno je kako se na osnovu krive iz tabele svih vrednosti senzitivnosti i specifičnosti može odrediti optimalan granični skor za neki test ili za potrebe klasifikovanja druge vrste. Pokazano je kako se u statističkom programu SPSS unose podaci i analiziraju dobijeni rezultati ROC analize. Takođe su ponuđeni i drugi programi i paketi koji omogućavaju ovu analizu sa brojnim dodatnim mogućnostima. Na kraju je ukazano na rezultate istraživanja u okviru kliničke psihologije koji su utemeljeni na ROC analizi i karakteristikama testa, odnosno klasifikacije na kojima je ova analiza utemeljena.

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Kochański, Błażej. "Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data." Risks 10, no. 10 (September 20, 2022): 184. http://dx.doi.org/10.3390/risks10100184.

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In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data in order to determine which models could be used in credit-risk-management practice. We list several theoretical models for an ROC curve and describe them in the credit-scoring context. The model list includes the binormal, bigamma, bibeta, bilogistic, power, and bifractal curves. The models are then tested against empirical credit-scoring ROC data from publicly available presentations and papers, as well as from European retail lending institutions. Except for the power curve, all the presented models fit the data quite well. However, based on the results and other favourable properties, it is suggested that the binormal curve is the preferred choice for modelling credit-scoring ROC curves.

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Ransing, Ramdas S., Neha Gupta, Girish Agrawal, and Nilima Mahapatro. "Platelet and Red Blood Cell Indices in Patients with Panic Disorder: A Receiver Operating Characteristic Analysis." Journal of Neurosciences in Rural Practice 11, no. 02 (March 17, 2020): 261–66. http://dx.doi.org/10.1055/s-0040-1703422.

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Abstract Objective Panic disorder (PD) is associated with changes in platelet and red blood cell (RBC) indices. However, the diagnostic or predictive value of these indices is unknown. This study assessed the diagnostic and discriminating value of platelet and RBC indices in patients with PD. Materials and Methods In this cross-sectional study including patients with PD (n = 98) and healthy controls (n = 102), we compared the following blood indices: mean platelet volume (MPV), platelet distribution width (PDW), and RBC distribution width (RDW). The receiver operating characteristic (ROC) curve was used to calculate the area under the ROC curve (AUC), sensitivity, specificity, and likelihood ratio for the platelet and RBC indices. Results Statistically significant increase in PDW (17.01 ± 0.91 vs. 14.8 ± 2.06; p < 0.0001) and RDW (16.56 ± 2.32 vs. 15.12 ± 2.43; p < 0.0001) levels were observed in patients with PD. PDW and mean corpuscular hemoglobin concentration had larger AUC (0.89 and 0.74, respectively) and Youden’s index (0.65 and 0.39, respectively), indicating their higher predictive capacity as well as higher sensitivity in discriminating patients with PD from healthy controls. Conclusion PDW can be considered a “good” diagnostic or predictive marker in patients with PD.

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Gönen, Mithat, and Glenn Heller. "Lehmann Family of ROC Curves." Medical Decision Making 30, no. 4 (March 30, 2010): 509–17. http://dx.doi.org/10.1177/0272989x09360067.

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Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on the Lehmann family, also known as the proportional hazards specification. The resulting ROC curve and its functionals (such as the area under the curve and the sensitivity at a given level of specificity) have simple analytic forms. Closed-form expressions for the functional estimates and their corresponding asymptotic variances are derived. This family accommodates the comparison of multiple markers, covariate adjustments, and clustered data through a regression formulation. Evaluation of the underlying assumptions, model fitting, and model selection can be performed using any off-the-shelf proportional hazards statistical software package.

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KURTCEPHE, MURAT, and H. ALTAY GÜVENIR. "A DISCRETIZATION METHOD BASED ON MAXIMIZING THE AREA UNDER RECEIVER OPERATING CHARACTERISTIC CURVE." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 01 (February 2013): 1350002. http://dx.doi.org/10.1142/s021800141350002x.

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Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the receiver operating characteristics (ROC) Curve (AUC) measure. Maximum area under ROC curve-based discretization (MAD) is a global, static and supervised discretization method. MAD uses the sorted order of the continuous values of a feature and discretizes the feature in such a way that the AUC based on that feature is to be maximized. The proposed method is compared with alternative discretization methods such as ChiMerge, Entropy-Minimum Description Length Principle (MDLP), Fixed Frequency Discretization (FFD), and Proportional Discretization (PD). FFD and PD have been recently proposed and are designed for Naïve Bayes learning. ChiMerge is a merging discretization method as the MAD method. Evaluations are performed in terms of M-Measure, an AUC-based metric for multi-class classification, and accuracy values obtained from Naïve Bayes and Aggregating One-Dependence Estimators (AODE) algorithms by using real-world datasets. Empirical results show that MAD is a strong candidate to be a good alternative to other discretization methods.

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Cleves, Mario A. "Comparative Assessment of Three Common Algorithms for Estimating the Variance of the Area under the Nonparametric Receiver Operating Characteristic Curve." Stata Journal: Promoting communications on statistics and Stata 2, no. 3 (September 2002): 280–89. http://dx.doi.org/10.1177/1536867x0200200304.

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The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictive power of statistical models for binary outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do not directly estimate its variance. Three algorithms for computing the variance for the area under the nonparametric ROC curve are commonly used, although ambiguity exists about their behavior under diverse study conditions. Using simulated data, we found similar asymptotic performance between these algorithms when the diagnostic test produces results on a continuous scale, but found notable differences in small samples, and when the diagnostic test yields results on a discrete diagnostic scale.

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Swensson, Richard G., Jill L. King, and David Gur. "A constrained formulation for the receiver operating characteristic (ROC) curve based on probability summation." Medical Physics 28, no. 8 (August 2001): 1597–609. http://dx.doi.org/10.1118/1.1382604.

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Lora, David, Israel Contador, José F. Pérez-Regadera, and Agustín Gómez de la Cámara. "Features of the Area under the Receiver Operating Characteristic (ROC) Curve. A Good Practice." Stata Journal: Promoting communications on statistics and Stata 16, no. 1 (March 2016): 185–96. http://dx.doi.org/10.1177/1536867x1601600115.

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Xu, Weichao, Jisheng Dai, Y. S. Hung, and Qinruo Wang. "Estimating the area under a receiver operating characteristic (ROC) curve: Parametric and nonparametric ways." Signal Processing 93, no. 11 (November 2013): 3111–23. http://dx.doi.org/10.1016/j.sigpro.2013.05.010.

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Centor, Robert M. "A Visicalc Program for Estimating the Area Under a Receiver Operating Characteristic (ROC) Curve." Medical Decision Making 5, no. 2 (June 1985): 139–48. http://dx.doi.org/10.1177/0272989x8500500203.

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Storey, Joella E., Jeffrey T. J. Rowland, David Basic, and David A. Conforti. "A comparison of five clock scoring methods using ROC (receiver operating characteristic) curve analysis." International Journal of Geriatric Psychiatry 16, no. 4 (2001): 394–99. http://dx.doi.org/10.1002/gps.352.

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Johnson, Nils P. "Advantages to transforming the receiver operating characteristic (ROC) curve into likelihood ratio co-ordinates." Statistics in Medicine 23, no. 14 (2004): 2257–66. http://dx.doi.org/10.1002/sim.1835.

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Stephan, Carsten, Sebastian Wesseling, Tania Schink, and Klaus Jung. "Comparison of Eight Computer Programs for Receiver-Operating Characteristic Analysis." Clinical Chemistry 49, no. 3 (March 1, 2003): 433–39. http://dx.doi.org/10.1373/49.3.433.

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Abstract Background: ROC analysis is widely accepted to assess and compare diagnostic validity of laboratory tests. Within the last few years, many new ROC programs have become available but have not been systematically evaluated. The aim of this study was to assess different ROC programs regarding their ease of use, mathematical correctness, final output, and their compatibility with other graphics programs. Methods: Eight available programs running under Windows (AccuROC, Analyse-It, CMDT, GraphROC, MedCalc, mROC, ROCKIT, and SPSS) were evaluated. ROC analyses of prostate-specific antigen and related values were performed from a dataset of 928 men with prostate cancer and benign prostatic hyperplasia and corresponding subsets. Criteria such as data input, data output, and correctness and completeness of results were used to evaluate the practicability of the programs. Results: Although the programs produced equivalent results (areas under the curves and their characteristics), we observed deficiencies concerning input of data, processing of the output data, and completeness of the results. Analyse-It, AccuROC, and MedCalc exhibited good performance, but each program had different shortcomings. Only GraphROC could compare curves at a certain sensitivity or specificity cutoff. Conclusions: Adequate ROC analysis and ROC plotting cannot be performed with a single program. Analyse-It, AccuROC, and MedCalc can be recommended with certain limitations. Further improvements of the programs are necessary.

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Wei, Chih Chiang. "Receiver Operating Characteristic for Diagnosis of Wine Quality by Bayesian Network Classifiers." Advanced Materials Research 591-593 (November 2012): 1168–73. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1168.

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This paper is dedicated to demonstrate the use of the receiver operating characteristic (ROC) and the area under the ROC curve (AUC) for diagnosing forecast skill. Several local search heuristic algorithms to discover which one performs better for learning a certain Bayesian networks (BN). Five heuristic search algorithms, including K2, Hill Climbing, Repeated Hill Climber, LAGD Hill Climbing, and TAN, were empirically evaluated and compared. This study tests BN models in a real-world case, the Vinho Verde wine taste preferences. An average AUC of 0.746 and 0.727 respectively in red wine and white wine were obtained by TAN algorithm. The results show that the use of TAN can effectively improve the AUC measures for predicting quality grade.

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