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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).