title: Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System.
abstract: BACKGROUND
  Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data.
  
  
  METHODS
  This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model.
  
  
  RESULTS
  Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%).
  
  
  CONCLUSIONS
  Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.
authors.affiliation: Department of Environmental and Energy, Islamic Azad University, Science and Research Branch, Tehran, Iran.
authors.forename: Jamshid
authors.initials: J
authors.lastname: Norouzi
authors.affiliation: Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 61357-33118, Iran.
authors.forename: Ali
authors.identifier: 0000-0002-1216-2109
authors.initials: A
authors.lastname: Yadollahpour
authors.affiliation: Department of Civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, Iran.
authors.forename: Seyed Ahmad
authors.initials: SA
authors.lastname: Mirbagheri
authors.affiliation: Iranian Tissue Bank & Research Center, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mitra Mahdavi
authors.initials: MM
authors.lastname: Mazdeh
authors.affiliation: Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 61357-33118, Iran.
authors.forename: Seyed Ahmad
authors.initials: SA
authors.lastname: Hosseini
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001794:Blood Pressure; D002118:Calcium / Q000097:blood; D003401:Creatine / Q000097:blood; D018450:Disease Progression; D005103:Expert Systems; D005260:Female; D017143:Fuzzy Logic; D005919:Glomerular Filtration Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D010758:Phosphorus / Q000097:blood; D051437:Renal Insufficiency / Q000175:diagnosis* / Q000473:pathology*; D051436:Renal Insufficiency, Chronic / Q000175:diagnosis* / Q000473:pathology*; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors
chemical_list: D010758:Phosphorus; D003401:Creatine; D002118:Calcium