Design Methodology of a Fuzzy Knowledgebase System to predict the risk of Diabetic Nephropathy
The main objective of the design methodology
of a Fuzzy knowledgebase System is to
predict the risk of Diabetic Nephropathy in
terms of Glomeruler Filtration Rate (GFR). In
this paper, the controllable risk factors
"Hyperglycemia, Insulin, Ketones, Lipids,
Obesity, Blood Pressure and
Protein/Creatinine ratio" are considered as
input parameters and the "stages of renal
disorder" is the output parameter. The input
triangular membership functions are Low,
Normal, High and Very High and the output
triangular membership functions are s1, s2, s3,
s4 and s5. As the renal complications are now
the leading causes of diabetes-related
morbidity and mortality, a FKBS is designed
to perform the optimum control on high risk
controllable risk factors by acquiring and
interpreting the medical experts' knowledge.
Fuzzy logic is used to incorporate the
available knowledge into intelligent control
system based on the medical experts'
knowledge and clinical observations. The
proposed FKBS is validated with MatLab, and
is used as a tracking system with accuracy and
robustness. The FKBS captures the existence
of uncertainty in the risk factors of Diabetic
Nephropathy, resolves the renal failure with
optimum result and protects the patients from
End Stage Renal Disorder (ESRD).
Keywords: Diabetes Mellitus, GFR, ESRD,
Uncertainty, Fuzzy knowledgebase System,
Inference Engine
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