Coronary heart disease is a major cause of morbidity and mortality in the modern society. Many risk factors for coronary heart disease have been discussed and identified by medical fraternity. However, the magnitudes of risk factors, particularly in predicting the disease are remained unknown and inconclusive. The purpose of this study was to develop fuzzy regression prediction model and to inv...

Background Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. Materials and Methods We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morp...

In machining industry, cutting fluids have an important role due to their lubrication, cooling and chip removal functions. Using of cutting fluids can improve machining process efficiency, tool life, surface quality and it can reduce cutting forces and vibrations. However, health and environmental problems are encountered with the use of cutting fluids. Therefore, there has been a high demand f...

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression an...

This paper explores the use of fuzzy membership values generated by fuzzy c-means clustering (FCM) method to predict soil properties over space. A weighted average model was used on fuzzy membership to get soil properties. To validate the efficiency of this model, it was then compared with a multiple linear regression model between the soil property and terrain attributes. Four indices were cal...

Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great amplitude of wide variety. We verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in ord...

In this paper, a neurofuzzy adaptive control framework for discrete-time systems based on kernel smoothing regression is developed. Kernel regression is a nonparametric statistics technique used to determine a regression model where no model assumption has been done. Due to similarity with fuzzy systems, kernel smoothing is used to obtain knowledge about the structure of the fuzzy system and th...

The fuzzy regression has been found effective in modeling the relationship between the dependent variable and independent variables when a high degree of fuzziness is involved and only a few data sets are available for model building. This research, therefore, proposes an approach for optimizing multiple responses in the Taguchi method using fuzzy regression and desirability function. The stati...