Employing spatial regression models to fuzzy and un fuzzy data for factors affecting breast cancer incidence in Iraq

Abstract

To analyze data with The spatial effect of the data, the concept of spatial regression models came as a new method for analyzing spatial data, which relies on analyzing data on location instead of years to study the direct effects of a group of influencing factors, as a particular phenomenon is affected by many factors that have a direct impact on the phenomenon. In this research, we used breast cancer incidence data, the most important factors affecting the incidence, and the use of spatial regression models to analyze the data, including: the autoregressive model (SAR), the spatial error model, SEM, and the fuzzy autoregressive model, FSAR. For the purpose of estimating the parameters of the regression models, we used two methods:  the ordinary least squares method and the maximum likelihood method, For comparison of the methods we used mean squares error ,  The data collected for patients were also blurred and research methods were used to determine which model is the best. The most important conclusions were that the method of greatest possibility of the SAR model is the best according to the size of the sample used and the number of explanatory variables.