Abstract
Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the 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 and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and 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 user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed.
Highlights
And differential diagnosis or prediction of disease progression and improving diagnosis reliability are today becoming prerequisite needs in medicine and health systems (Anagnostou et al, 2003; Al-Shayea et al, 2013)
Medical decision support systems (MDSSs) are intelligent and expert systems used in medicine to help medical experts make appropriate decisions in different fields (Smith et al, 2003)
In this study we propose a MDSS based on the adaptive neuro-fuzzy inference system (ANFIS) system to predict renal failure progression
Summary
And differential diagnosis or prediction of disease progression and improving diagnosis reliability are today becoming prerequisite needs in medicine and health systems (Anagnostou et al, 2003; Al-Shayea et al, 2013). MYCIN is one of the early clinical decision systems developed at Stanford University in the early 1970s This DSS was an expert system based on artificial intelligence to identify bacteria causing severe infections in order to recommend specific antibiotics for patients (Buchanan and Shortliffe, 1984). The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure
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