the effects of the underlying disease and serum albumin on gfr prediction using the adaptive neuro fuzzy inference system (anfis)

introduction: kidney disease is a major public health challenge worldwide. epidemiologic data suggest a significant relationship between underlying diseases and decrease in glomerular filtration rate (gfr). clinical studies and laboratory research have shown that the mentioned parameter is effective in development and progression of the renal disease per se. in this study, we used learning-based system based on the neural network concepts. method: to predict gfr and propose an intelligent method with few errors (about 3%), we need to prognosticate the course and severity of the kidney disease in patients with chronic kidney disease using limited data and information. adaptive neuro fuzzy inference system (anfis) used in the present study is based on the model proposed by jang, and all laboratory (creatinine, calcium, phosphorus, albumin) and underlying disease caused by chronic kidney disease ( ckd ) were reviewed. results: it has been shown that the rate of gfr decreases in patients with diabetes and glomerulopathy was faster than other causes. furthermore, serum albumin level less than 4.5gr/dl with diabetes was also associated with higher risk of rapid gfr loss. conclusion:therefore, it seems that this modeling of fuzzy variables with error less than 3.5% in some cases and create a fuzzy inference system model that presents the complex relationships between the laboratory input variables and gfr as simple linear models.