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Diagnostic Odds Ratio
In medical testing with binary classification, the diagnostic odds ratio (DOR) is a Evaluation of binary classifiers, measure of the effectiveness of a diagnostic test. It is defined as the ratio of the odds of the test being positive if the subject has a disease relative to the odds of the test being positive if the subject does not have the disease. There is also a Multi-class classification, multiclass version of the diagnostic odds ratio. The rationale for the diagnostic odds ratio is that it is a single indicator of test performance (like Accuracy#Accuracy and precision in binary classification, accuracy and Youden's J statistic) but which is independent of prevalence (unlike accuracy) and is presented as an odds ratio, which has theoretical advantages (e.g. Bayes Law is written a product of ratios). Definition The diagnostic odds ratio is defined mathematically as: :\text = \frac = \frac = \frac where TP, FN, FP and TN are the number of true positives, false negatives, f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Positive Predictive Value
The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test (as true positive rate and true negative rate are); they depend also on the prevalence. Both PPV and NPV can be derived using Bayes' theorem. Although sometimes used synonymously, a ''positive predictive value'' generally refers to what is established by control groups, while a post-test probability refers to a probability for an individual. Still, if the individual's pre-test probability of the target condition is the same as the prevalence in the control group used to establish the positive predictive value, the two are numerically equal. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Binary Classification
Binary classification is the task of classifying the elements of a set into one of two groups (each called ''class''). Typical binary classification problems include: * Medical testing to determine if a patient has a certain disease or not; * Quality control in industry, deciding whether a specification has been met; * In information retrieval, deciding whether a page should be in the result set of a search or not * In administration, deciding whether someone should be issued with a driving licence or not * In cognition, deciding whether an object is food or not food. When measuring the accuracy of a binary classifier, the simplest way is to count the errors. But in the real world often one of the two classes is more important, so that the number of both of the different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a '' false positive'') is considered differently from not detecting a disease when it is present (a '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sensitivity And Specificity
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: * Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive. * Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test" which is assumed correct. For all testing, both diagnoses and screening, there is usually a trade-off between sensitivity and specificity, such that higher sensiti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Contingency Table
In statistics, a contingency table (also known as a cross tabulation or crosstab) is a type of table in a matrix format that displays the multivariate frequency distribution of the variables. They are heavily used in survey research, business intelligence, engineering, and scientific research. They provide a basic picture of the interrelation between two variables and can help find interactions between them. The term ''contingency table'' was first used by Karl Pearson in "On the Theory of Contingency and Its Relation to Association and Normal Correlation", part of the '' Drapers' Company Research Memoirs Biometric Series I'' published in 1904. A crucial problem of multivariate statistics is finding the (direct-)dependence structure underlying the variables contained in high-dimensional contingency tables. If some of the conditional independences are revealed, then even the storage of the data can be done in a smarter way (see Lauritzen (2002)). In order to do this one can use in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gold Standard (test)
In medicine and medical statistics, the gold standard, criterion standard, or reference standard is the diagnostic test or benchmark that is the best available under ''reasonable'' conditions. It is the test against which new tests are compared to gauge their validity, and it is used to evaluate the efficacy of treatments. The meaning of "gold standard" may differ between practical medicine and the statistical ideal. With some medical conditions, only an autopsy can guarantee diagnostic certainty. In these cases, the gold standard test is the best test that keeps the patient alive, and even gold standard tests can require follow-up to confirm or refute the diagnosis. History The term 'gold standard' in its current sense in medical research was coined by Rudd in 1979, in reference to the monetary gold standard. In medicine "Gold standard" can refer to popular clinical endpoints by which scientific evidence is evaluated. For example, in resuscitation research, the "gold standar ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Confusion Matrix
In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one; in unsupervised learning it is usually called a matching matrix. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa – both variants are found in the literature. The diagonal of the matrix therefore represents all instances that are correctly predicted. The name stems from the fact that it makes it easy to see whether the system is confusing two classes (i.e. commonly mislabeling one as another). It is a special kind of contingency table, with two dimensions ("actual" and "predicted"), and identical sets of "classes" in both dimensions (each combination of dimension and class is a variable in the contingency table). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Receiver Operating Characteristic
A receiver operating characteristic curve, or ROC curve, is a graph of a function, graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values. ROC analysis is commonly applied in the assessment of diagnostic test performance in clinical epidemiology. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting. The ROC can also be thought of as a plot of the statistical power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function of false positive rate. Given that the probability distributions for both true positive and false positive are known, the ROC curve is obtained as the cumulative distribution function (CDF, area under the probability ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Logit
In statistics, the logit ( ) function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning, especially in Data transformation (statistics), data transformations. Mathematically, the logit is the inverse function, inverse of the logistic function, standard logistic function \sigma(x) = 1/(1+e^), so the logit is defined as : \operatorname p = \sigma^(p) = \ln \frac \quad \text \quad p \in (0,1). Because of this, the logit is also called the log-odds since it is equal to the logarithm of the odds \frac where is a probability. Thus, the logit is a type of function that maps probability values from (0, 1) to real numbers in (-\infty, +\infty), akin to the probit, probit function. Definition If is a probability, then is the corresponding odds; the of the probability is the logarithm of the odds, i.e.: : \operatorname(p)=\ln\left( \frac \right) =\ln(p)-\ln(1-p)=-\ln\left( \frac-1\right)=2\operatornam ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Trade-off
A trade-off (or tradeoff) is a situational decision that involves diminishing or losing on quality, quantity, or property of a set or design in return for gains in other aspects. In simple terms, a tradeoff is where one thing increases, and another must decrease. Tradeoffs stem from limitations of many origins, including simple physics – for instance, only a certain volume of objects can fit into a given space, so a full container must remove some items in order to accept any more, and vessels can carry a few large items or multiple small items. Tradeoffs also commonly refer to different configurations of a single item, such as the tuning of strings on a guitar to enable different notes to be played, as well as an allocation of time and attention towards different tasks. The concept of a tradeoff suggests a tactical or strategic choice made with full comprehension of the advantages and disadvantages of each setup. An economic example is the decision to invest in stocks, which ar ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inverse-variance Weighting
In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance (i.e., proportional to its precision). Given a sequence of independent observations with variances , the inverse-variance weighted average is given by : \hat = \frac . The inverse-variance weighted average has the least variance among all weighted averages, which can be calculated as : Var(\hat) = \frac . This variance can be used to parametrize a confidence interval. If the variances of the measurements are all equal, then the inverse-variance weighted average becomes the simple average. Inverse-variance weighting is typically used in statistical meta-analysis or sensor fusion to combine the results from independent measurements. Context Suppose an experimenter wishes to measure the value of a quantity, say the acceleration due to gravity of Earth, whose true ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
