‎fuzzy logistic regression analysis using the least squares method

One of the most efficient statistical tools for modeling the relationship between a dependent variable and several independent variables is regression‎. ‎in practice‎, ‎observations relating to one or more variables‎, ‎or the relationship between variables‎, ‎may be vague or non-specific‎. ‎in such cases‎, ‎classic regression methods will not have enough capability to model data‎, ‎and one of the alternative methods is regression in a fuzzy environment‎. ‎the fuzzy logistic regression model provides a framework in the fuzzy environment to investigate the relationship between a binary response variable and a set of covariates‎. ‎the purpose of this paper is to attempt to develop a fuzzy model that is based on the idea of the possibility of success‎. ‎these possibilities are characterized {by several} linguistic phrases‎, ‎including low‎, ‎medium‎, ‎and high‎, ‎among others‎. ‎next‎, ‎we {use a set of precise explanatory variable observations to model the logarithm transformation of‎ ‎possibilistic odds.‎ ‎we assume that the model's parameters are triangular fuzzy numbers.} we use the least squares method in fuzzy linear regression to estimate the parameters of the provided model‎. ‎we compute three types of goodness-of-fit criteria to evaluate the model‎. ‎ultimately‎, ‎we model suspected cases of systemic lupus erythematosus (sle) disease based on significant risk factors to identify the model's application‎. ‎we do this due to the widespread use of logistic regression in clinical studies and the prevalence of ambiguous observations in clinical diagnosis‎. ‎furthermore‎, ‎to assess the prevalence of diabetes in the community‎, ‎we will collect a sample of plasma glucose levels‎, ‎measured two hours after a meal‎, ‎from each participant in a clinical survey‎. ‎the proposed model has the potential to rationally replace an ordinary model in modeling the clinically ambiguous condition‎, ‎according to the findings‎.