FUZZY LOGISTIC REGRESSION: A NEW POSSIBILISTIC MODEL AND ITS APPLICATION IN CLINICAL VAGUE STATUS

Authors

  • S. Mahmoud Taheri Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, 84156-83111, Iran
  • Saeedeh Pourahmad Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, 71345-1874, Iran
Abstract:

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 observations and disagreements among scientists in defining diseasecriteria have led to vague diagnosis. Also,specialists oftenreport their opinion in linguistic terms rather than numerically. Usually, because of these  limitations, the assumptions of the statistical model do not hold and hence their use is questionable. We therefore need to develop new methods formodeling and analyzing the problem. In this study, a model called the  `` fuzzy logistic model '' isproposed for the case when the explanatory variables arecrisp and the value of the binary response variable is reportedas a number between zero and one (indicating the possibility ofhaving the property). In this regard, the concept of `` possibilistic odds '' is alsointroduced. Then, the methodology and formulationof this model is explained in detail and a linear programming approach is use to estimate the model parameters. Some goodness-of-fit criteria are proposed and a numerical example is given as an example.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

fuzzy logistic regression: a new possibilistic model and its application in clinical vague status

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...

full text

A NEW APPROACH FOR PARAMETER ESTIMATION IN FUZZY LOGISTIC REGRESSION

Logistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situa...

full text

Fuzzy Regression Model and Its Application: A Review

Fuzzy regression model has been widely used in recent years throughout the globe. In view of this, an attempt has been made in this research paper to present the review of fuzzy regression model for better estimation and prediction. The regression analysis is statistical tool used for prediction. As we know that the regression analysis follows Gaussian assumptions, sometimes dataset is too smal...

full text

Effect of hormone receptors and Her-2 status on metastasis status in patients with breast cancer using logistic regression model

Introduction: Breast cancer is the most common type of cancer and the leading cause of cancer death among women. According to estimates in 2018, the most frequency of cancer in Iranian women was related to breast cancer with 13776 (12.5%) new cases. Metastasis is a complication of this disease that occurs in 1%-5% of patients. The aim of this study was to investigate the effect of hormone recep...

full text

Fuzzy Class Logistic Regression Analysis

Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm...

full text

Model Selection in Logistic Regression and Performance of its Predic

Logistic regression studies often have several covariates and asked to cull these covariates to arrive at a parsimonious model. The goal is to maximize predictive power while minimizing the number of covariates in the model. Purposeful selection of covariates does not provide efficient model in case of large number of covariates while mechanical stepwise and best subsets selection procedures st...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 8  issue 1

pages  1- 17

publication date 2011-02-11

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023