The logistic regression model is one of the statistical models that widely used to analyze binary response data. Bayesian and maximum likelihood methods were used to model binary data for prostate cancer. In Bayesian method Gibbs sampling algorithm was used to select 5 millions samples from posterior distribution of logistic coefficients under flat non-informative prior. Then mean, standard dev...

Logistic regression models are frequently used in clinicalresearch and particularly for modeling disease status and patientsurvival. In practice, clinical studies have several limitationsFor instance, in the study of rare diseases or due ethical considerations, we can only have small sample sizes. In addition, the lack of suitable andadvanced measuring instruments lead to non-precise observatio...

background: we designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the artificial neural network (ann). materials and methods: the ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. each patient’s record consisted of 6 s...

background: the best management for an ovarian mass is provided by appropriate prediction of malignancy. the aim of our study is to construct and validate a new malignancy probability score based on four simple sonographic findings and age. methods: in a cross sectional study; histopathological files of 3303 ovarian mass patients and tertiary hospitals, have reviewed within 6 years (2000-2006)....

We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions. We focus on the setting where the covariance matrices for the two conditional distributions are the same. The corresponding generative model classifier, derived via the Bayes rule, also called Linear Discriminant Analysis, has been shown...

A statistical generative model is presented as an alternative to negative selection in anomaly detection of string data. We extend the probabilistic approach to binary classification from fixed-length binary strings into variable-length strings from a finite symbol alphabet by fitting a mixture model of multinomial distributions for the frequency of adjacent symbols. Robust and localized change...

logistic regression models are frequently used in clinicalresearch and particularly for modeling disease status and patientsurvival. in practice, clinical studies have several limitationsfor instance, in the study of rare diseases or due ethical considerations, we can only have small sample sizes. in addition, the lack of suitable andadvanced measuring instruments lead to non-precise observatio...