Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus

A fuzzy expert system to assess the nephropathy control is proposed.The success rate of the fuzzy expert system is 93.33%.The use of this system allows reduce the lack of control and wrong treatments.The system would be useful if it is adapted to the diagnosis of other pathologies.Recognition of predictor variables of lack of control avoid irreversible damage in kidneys. Diabetic nephropathy is a life-threatening complication if not controlled properly. Early detection and effective control prevent its progression. In this study, the development of a Fuzzy Expert System (FES) is proposed to help doctors assess the nephropathy control in patients with Type 2 Diabetes Mellitus (T2DM). The study is based on a FES that was developed with the use of Clinical Practice Guidelines (CPG), data bases and the expertise of a team of doctors. It considers the use of input variables such as Glomerular Filtration Rate (GFR), serum creatinine, blood glucose, Type 2 Diabetes Mellitus Age (T2DMA), uric acid, hypertension and dyslipidemia. All these factors, give an efficient nephropathy control assessment. Sixty tests were performed using the expertise of a team of doctors, the expected results were compared with those estimated by the FES (using the same cases), and it was observed that the FES succeeds in up to 93.33% of the cases. The response surface analysis shows that GFR, serum creatinine and hypertension have a greater impact in the nephropathy control. This system supports the doctors in nephropathy control, but it does not estimate the renal failure stages. Nephropathy control is a clinical problem that includes uncertainty and inaccuracy, so the use of a FES is recommended to overcome this problem, since fuzzy systems help to assess the inherent uncertainty degree.

[1]  E. Mancini,et al.  Prevention of dialysis hypotension episodes using fuzzy logic control system. , 2007, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[2]  Shu-Hsien Liao,et al.  Expert system methodologies and applications - a decade review from 1995 to 2004 , 2005, Expert Syst. Appl..

[3]  N. Madias,et al.  Serum creatinine as an index of renal function: new insights into old concepts. , 1992, Clinical chemistry.

[4]  Marco Antonio León Mazón,et al.  Eficacia del programa de educación en diabetes en los parámetros clínicos y bioquímicos , 2013 .

[5]  Kdoqi KDOQI Clinical Practice Guidelines and Clinical Practice Recommendations for Diabetes and Chronic Kidney Disease. , 2007, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[6]  Ethan M Balk,et al.  Erratum: National Kidney Foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification (Annals of Internal Medicine (2003) 139 (137-147)) , 2003 .

[7]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[8]  José Raymundo Rodríguez-Moctezuma,et al.  Estilo de vida y control metabólico en diabéticos del programa DiabetIMSS , 2014 .

[9]  J. Gross,et al.  Diabetic nephropathy: diagnosis, prevention, and treatment. , 2005, Diabetes care.

[10]  J.R. Perez-Gallardo,et al.  Interpretation of Mammographic Using Fuzzy Logic for Early Diagnosis of Breast Cancer , 2008, 2008 Seventh Mexican International Conference on Artificial Intelligence.

[11]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[12]  Jerry Yee,et al.  Automation, decision support, and expert systems in nephrology. , 2008, Advances in chronic kidney disease.

[13]  Rama Devi,et al.  Design Methodology of a Fuzzy Knowledgebase System to predict the risk of Diabetic Nephropathy , 2010 .

[14]  César Montes,et al.  An expert system for homeopathic glaucoma treatment (SEHO) , 1995 .

[15]  A. Akobeng,et al.  Confidence intervals and p‐values in clinical decision making , 2008, Acta paediatrica.

[16]  Moisés Hernández,et al.  How frequently the clinical practice recommendations for nephropathy are achieved in patients with type 2 diabetes mellitus in a primary health-care setting? , 2008, Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion.

[17]  Cameron Js,et al.  Uric Acid and Renal Disease , 2006 .

[18]  Eugene Roventa,et al.  The diagnosis of some kidney diseases in a small prolog Expert System , 2009, 2009 3rd International Workshop on Soft Computing Applications.

[19]  R. Ruelas,et al.  A defuzzification method respecting the fuzzification , 1997, Fuzzy Sets Syst..

[20]  Anthony Barnett,et al.  Prevention of loss of renal function over time in patients with diabetic nephropathy. , 2006, The American journal of medicine.

[21]  J. Selby,et al.  The natural history and epidemiology of diabetic nephropathy. Implications for prevention and control. , 1990, JAMA.

[22]  Rashedur M. Rahman,et al.  Diagnosis of kidney disease using fuzzy expert system , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).

[23]  Osman N. Uçan,et al.  Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System , 2010, Journal of Medical Systems.

[24]  Rubén Posada-Gómez,et al.  Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation , 2013, Comput. Math. Methods Medicine.

[25]  Randolph A. Miller,et al.  Review: Medical Diagnostic Decision Support Systems - Past, Present, And Future: A Threaded Bibliography and Brief Commentary , 1994, J. Am. Medical Informatics Assoc..

[26]  Almudena Laris González,et al.  Prevalencia, factores de riesgo y consecuencias de la referencia tardía al nefrólogo , 2011 .

[27]  Hossein Javidnia,et al.  An Approach for Recommendations in Self Management of Diabetes based on Expert System , 2012 .

[28]  Nigel H. Lovell,et al.  Erratum to “Optimisation of a Generic Ionic Model of Cardiac Myocyte Electrical Activity” , 2013, Comput. Math. Methods Medicine.

[29]  G. Eknoyan,et al.  National Kidney Foundation Practice Guidelines for Chronic Kidney Disease: Evaluation, Classification, and Stratification , 2003, Annals of Internal Medicine.

[30]  Ethan M Balk,et al.  K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. , 2002, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[31]  A. Malathi,et al.  Fuzzy logic system for risk-level classification of diabetic nephropathy , 2014, 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE).

[32]  Vilém Novák,et al.  Submitted/to appear: Fuzzy Sets and Systems Supported by: , 2022 .

[33]  K. Siamopoulos,et al.  Dyslipidemia in Chronic Kidney Disease: An Approach to Pathogenesis and Treatment , 2008, American Journal of Nephrology.

[34]  Giuseppe Remuzzi,et al.  Nephropathy in Patients with Type 2 Diabetes , 2002 .

[35]  Marco Lucarelli,et al.  Interpretable Fuzzy Modeling for Decision Support in IgA Nephropathy , 2011, WILF.

[36]  Almudena Laris-González,et al.  Prevalencia, factores de riesgo y consecuencias de la referencia tardía al nefrólogo , 2011 .

[37]  Marco Antonio León-Mazón,et al.  DiabetIMSS. Eficacia del programa de educación en diabetes en los parámetros clínicos y bioquímicos , 2013 .

[38]  Adam E. Gaweda,et al.  Fuzzy rule-based approach to automatic drug dosing in renal failure , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[39]  J Ludvigsson,et al.  Declining incidence of nephropathy in insulin-dependent diabetes mellitus. , 1994, The New England journal of medicine.

[40]  Beth A Sproule,et al.  Fuzzy pharmacology: theory and applications. , 2002, Trends in pharmacological sciences.