Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression

Background and objective: Chronic kidney disease (CKD) has covert nature in early stages postponing its diagnosis. Early diagnosis can reduce or even prevent the progression of renal damages. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro fuzzy inference system (ANFIS) to predict timeframe of renal failure. Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients which considers the threshold value of 15 cc/kg/min/1.73m2 of GFR as the marker of renal failure. Following evaluating 10 variables, the ANFIS model uses the weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a friendly-use graphical user interface of the model was built in MATLAB by which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the outputl. Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that the MDSS could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months with normalized mean absolute error lower than 4%. 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. Conclusions: The MDSS GUI can be used in medical centers can be used by medical centers and experts to predict the renal failure progression and through taking effective actions, the CKD can be prevented or effectively delayed.