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Predicting Renal Failure Progression in Chronic Kidney Disease using Integrated Intelligent Fuzzy Expert System


Affiliations
1 Department of Environmental and Energy, Islamic Azad University, Science and Research Branch, Tehran, Iran, Islamic Republic of
2 Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 61357-33118, Iran, Islamic Republic of
3 Department of Civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, Iran, Islamic Republic of
4 Iranian Tissue Bank & Research Center, Tehran University of Medical Sciences, Tehran, Iran, Islamic Republic of
5 Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 61357-33118, Iran, Islamic Republic of
 

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/1.73m2 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.
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  • Predicting Renal Failure Progression in Chronic Kidney Disease using Integrated Intelligent Fuzzy Expert System

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Authors

Jamshid Norouzi
Department of Environmental and Energy, Islamic Azad University, Science and Research Branch, Tehran, Iran, Islamic Republic of
Ali Yadollahpour
Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 61357-33118, Iran, Islamic Republic of
Seyed Ahmad Mirbagheri
Department of Civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, Iran, Islamic Republic of
Mitra Mahdavi Mazdeh
Iranian Tissue Bank & Research Center, Tehran University of Medical Sciences, Tehran, Iran, Islamic Republic of
Seyed Ahmad Hosseini
Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 61357-33118, Iran, Islamic Republic of

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/1.73m2 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.