Logistic Discriminant Functions in Electroencephalography

A class of logistic-type discriminant functions

In two-group discriminant analysis, the Neyman-Pearson Lemma establishes that the ROC curve for an arbitrary linear function is everywhere below the ROC curve for the true likelihood ratio. The weighted area between these two curves can be used as a risk function for finding good discriminant functions. The weight function corresponds to the objective of the analysis, for example to minimize th...

Logistic discriminant analysis of breast cancer using ultrasound measurements

Background: Logistic discriminant method was applied to differentiate malignant from benign in a group of patients with proved breast lesions on the basis of ultrasonic parameters. Materials and Methods: Our database include 273 patients' ultrasonographic pictures consisting of 14 quantitative variables. The measured variables were ultrasound propagation velocity, acoustic impedence and attenua...

A Bayesian Kernel Logistic Discriminant Model: An Improvement to the Kernel Fisher's Discriminant

The Kernel Fisher’s Discriminant (KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. Its main ingredient is the kernel trick which allows the efficient computation of Fisher’s Linear Discriminant in feature space. However, it is assuming equal covariance structure for all transformed classes, which is not true in many applica...

Network Multiple Station Discriminant Functions

Screening for primary aldosteronism with a logistic multivariate discriminant analysis.

OBJECTIVE Primary aldosteronism (PA) is the most common endocrine cause of curable hypertension, but no single test unequivocally identifies it. Accordingly, we investigated the usefulness of a logistic multivariate discriminant analysis (MDA) approach for PA screening. DESIGN Generation of a logistic MDA function based on retrospective analysis of biochemical tests in a large cohort of refer...