**receiver**- This example shows how you can assess the performance of both coherent and noncoherent systems using receiver operating characteristic (ROC) curves. (mathworks.com)
- The detection performance is characterized by the area under the Receiver Operating Characteristic (ROC) Curve (AUC), which is a simple statistical performance measuring metric that varies between 0.5 and 1. (techrepublic.com)
- A receiver operating characteristic (ROC) curve graphically describes the performance of the classifier without the requirement of a threshold. (usda.gov)
- The receiver operating characteristic curve of the drain amylase or lipase concentration on each day was used to predict clinical PF (International Study Group on Pancreatic Fistula [ISGPF] grade B or C) and areas under the curves (AUC) were compared. (nih.gov)
- This type of graph is called a Receiver Operating Characteristic curve (or ROC curve. (unmc.edu)
- Receiver operating characteristic (ROC) curve analysis (Fig. 4) revealed the area under the ROC curve for VEGF was 0.788 (95% confidence interval, 0.711-0.864), whereas for MMP-9, the area under the ROC curve was 0.861 (95% confidence interval, 0.806-0.916). (nih.gov)
- Receiver operating characteristic (ROC) curve analyses.Total-anti-HBc (full line) and anti-HBc-IgM (dotted line). (nih.gov)
- In statistics, a receiver operating characteristic curve, i.e. (wikipedia.org)
- Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC (Receiver Operating Characteristic) curve analyses on metabolomic data sets. (wikipedia.org)
- In medical biomarker studies it is becoming increasingly common to report this tradeoff in sensitivity and specificity using a Receiver Operating Characteristic (ROC) curve. (wikipedia.org)
- 1. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. (wikipedia.org)
- Youden's index is often used in conjunction with receiver operating characteristic (ROC) analysis. (wikipedia.org)

**plot**- We can use the rocsnr function to calculate and plot ROC curves. (mathworks.com)
- Instead of individually calculating Pd and Pfa values for a given SNR, we can view the results in a plot of ROC curves. (mathworks.com)
- Calling the rocsnr function with an input vector of four SNR values and no output arguments produces a plot of the ROC curves. (mathworks.com)
- In the plot we can select the data cursor button in the toolbar (or in the Tools menu) and then select the SNR = 8 dB curve at the point where Pd = 0.9 to verify that Pfa is approximately 0.01. (mathworks.com)
- ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. (wikipedia.org)
- Since TPR is equivalent to sensitivity and FPR is equal to 1 − specificity, the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot. (wikipedia.org)
- ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on the x axis. (wikipedia.org)
- The value a can be used to plot a summary ROC (SROC) curve. (wikipedia.org)
- Procedures for method evaluation and method comparison include ROC curve analysis, Bland-Altman plot, as well as Deming and Passing-Bablok regression. (wikipedia.org)

**Receiving Operating Characteristic**- Receiving Operating Characteristic (ROC) curves are basically used in judgement of usefulness of diagnostic tests (in healthcare) or in wider sense in objective quantification of decision methods with two outcomes (like healthy or diseased in case of a diagnostic tool). (smart-statistics.com)

**binary classifiers**- Most of us use the ROC curve to assess our binary classifiers everyday. (jxieeducation.com)
- The 2 main properties outlined in this post make the ROC curve a fairly good way to compare binary classifiers. (jxieeducation.com)

**Characteristic**- The ROC is also known as a relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) as the criterion changes. (wikipedia.org)

**specificity**- The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). (wikipedia.org)
- ROC curves provide a simple visual method for one to determine the boundary limit (or the separation threshold) of a biomarker or a combination of biomarkers for the optimal combination of sensitivity and specificity. (wikipedia.org)

**diagonal**- The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. (unmc.edu)
- Since TPR and FPR are both p, a random classifier (baseline) will have a ROC curve of slope 1 (the diagonal) and an AUC of 0.5. (jxieeducation.com)
- It other words this is the J is the maximum vertical distance between the ROC curve and the diagonal. (smart-statistics.com)

**classifier**- To draw a ROC curve, only the true positive rate (TPR) and false positive rate (FPR) are needed (as functions of some classifier parameter). (wikipedia.org)

**Intervals**- In the univariate module single variables are evaluated (by a t-test) and ranked for their separation performance (i.e. the AUC of the ROC), including confidence intervals (CI) and a computed optimal threshold. (wikipedia.org)

**plots**- ROC curves are plots of the probability of detection (Pd) vs. the probability of false alarm (Pfa) for a given signal-to-noise ratio (SNR). (mathworks.com)
- The rocsnr function plots the ROC curves by default if no output arguments are specified. (mathworks.com)
- The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total income of the population (y axis) that is cumulatively earned by the bottom x% of the population (see diagram). (wikipedia.org)

**predictive**- ROC curve analysis with OptimalCutpoints, predictive values. (smart-statistics.com)
- Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. (wikipedia.org)

**analysis showed**- AUC of ROC analysis showed that drain pancreatic enzyme measurement POD 6 was the most appropriate diagnostic criterion for postoperative PF (Fig 1). (nih.gov)
- ROC analysis showed that the best cut-off for PCT in discriminating Enterobacteriaceae from nonfermentative Gram-negative bacteria was 3.1 ng/mL, with 90% sensitivity and 91% PPV (Figure 4). (nih.gov)

**area**- The template will also calculate the area under the curve (C14) and rate the accuracy of the test (C17. (qimacros.com)
- Its accuracy is measured by the area under the curve (AUC). (usda.gov)
- ROC curve analysis for predictability of pancreatic fistula according to the passage of postoperative days (A). A highest value of area under the curve (AUC) was obtained at postoperative day 6 (B). (nih.gov)
- The area under the curve is a measure of text accuracy. (unmc.edu)
- Area under the ROC curves for the levels of serum (A) VEGF and (B) MMP-9 in IDC patients and healthy individuals. (nih.gov)
- In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the cumulative distribution function (area under the probability distribution from − ∞ {\displaystyle -\infty } to the discrimination threshold) of the detection probability in the y-axis versus the cumulative distribution function of the false-alarm probability on the x-axis. (wikipedia.org)
- The AUC (area under the curve) of the ROC curve reflects the overall accuracy and the separation performance of the biomarker (or biomarkers), and can be readily used to compare different biomarker combinations or models. (wikipedia.org)
- K {\displaystyle K} denotes lifetime morbid risk, h L 2 {\displaystyle h_{L}^{2}} denotes heritability of liability, A U C {\displaystyle AUC} denotes area under the ROC curve. (wikipedia.org)
- The index is represented graphically as the height above the chance line, and it is also equivalent to the Area under the Curve subtended by a single operating point. (wikipedia.org)

**sensitivity**- The ROC curve is thus the sensitivity as a function of fall-out. (wikipedia.org)

**diagnostic**- Smooth of a ROC curve (sometimes the classification is based on a discrete scale (e.g. for a cancer diagnostic tool: "normal", "benign", "probably benign", "suspicious", "malignant") and smoothing methods fit a continuous curve based on the assumptions of the background distribution). (smart-statistics.com)
- ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. (wikipedia.org)
- The index is defined for all points of an ROC curve, and the maximum value of the index may be used as a criterion for selecting the optimum cut-off point when a diagnostic test gives a numeric rather than a dichotomous result. (wikipedia.org)

**Detection**- The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. (wikipedia.org)

**optimal**- ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. (wikipedia.org)
- In the multivariate module one can choose between three different techniques - SVM (support vector machine), PLS-DA (partial least squares discriminant analysis) and Random Forests for classifying and selecting metabolites or clinical variables for an optimal ROC performance. (wikipedia.org)

**assess**- ROC curves are often used to assess the performance of a radar or sonar detector. (mathworks.com)

**threshold**- The algorithm provides high quality binary images using entropy information of the images to define a primary threshold value which is adjusted with the use of ROC curves. (springer.com)
- The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. (wikipedia.org)

**prediction**- Each prediction result or instance of a confusion matrix represents one point in the ROC space. (wikipedia.org)

**metrics**- These are great theoretical advantages that other popular metrics (such as the precision-recall or the calibration curves) don't have. (jxieeducation.com)

**produces**- The resulting analysis produces the top-performing multi-variable model(s) based on their ROC curve characteristics. (wikipedia.org)

**statistical**- What are the statistical challenges in management of ROC curves? (smart-statistics.com)

**different**- The same value may result from many different distribution curves. (wikipedia.org)
- An image of different ROC curves is shown in Figure 1. (wikipedia.org)

**performance**- Examining a detector's ROC curves provides insight into its performance. (mathworks.com)
- ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards, meteorology, model performance assessment, and other areas for many decades and is increasingly used in machine learning and data mining research. (wikipedia.org)

**values**- One feature of the rocsnr function is that you can specify a vector of SNR values and rocsnr calculates the ROC curve for each of these SNR values. (mathworks.com)

**test**- Use a ROC Curve to choose the most appropriate 'cut-off' for a test. (qimacros.com)
- The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. (unmc.edu)
- A ROC curve starts at the (0,0) coordinate representing the case when all test results are negative and ends at (1,1) coordinate, representing that all test results are positive. (smart-statistics.com)

**select**- Click on the QI Macros Menu and select, Chart Templates, ROC Curve. (qimacros.com)

**equivalent**- An alternative approach is to define the Gini coefficient as half of the relative mean absolute difference, which is mathematically equivalent to the Lorenz curve definition. (wikipedia.org)

**draw**- The template will perform the calculations and draw the ROC Curve. (qimacros.com)

**space**- A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative trade-offs between true positive (benefits) and false positive (costs). (wikipedia.org)

**Image**- Mello C.A.B., Costa A.H.M. (2005) Image Thresholding of Historical Documents Using Entropy and ROC Curves. (springer.com)

**rate**- So as we know, the x-axis of the ROC curve is the False Positive Rate (FPR) and the y-axis is the True Positive Rate (TPR). (jxieeducation.com)

**accuracy**- Unfortunately, I did not go over other properties such as linear correlation with accuracy , pareto optimality and relationships with the calibration curve . (jxieeducation.com)