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The Diagnosis of Diabetic Nephropathy using Neuro- Fuzzy Expert System
Objectives: To develop an improved expert system for the diagnosis of nephropathy. Methods/Statistical Analysis: To achieve this objective, data on the nephropathy is taken by specialist doctors in this domain and adaptive neuro-fuzzy technique is applied on it. Gaussian membership functions are attempted in the study and MATLAB is used to implement the expert system. Findings: This system succeeds up to 96.25% of the cases. The sensitivity, specificity and precision obtained from this system are 97.5%, 95% and 95.12%. These parameters are found out by comparing the output achieved from this system with the judgments made by experts in this area. Application/Improvements: This expert system can be applied in the situations where the patient is unable to get medical assistance from doctor due to certain problems like low ratio of doctor to patient, unavailability of doctors in undeveloped areas etc.
Diagnosis, Expert System, Nephropathy, Neuro-Fuzzy
- Bojestig M, Arnqvist HJ, Hermanson G, Karlberg, BE, Ludvigsson J. Declining incidence of nephropathy in insulindependent diabetes mellits. The New England Journal of Medicine. 1994; 330(1):15-8. Crossref. PMid:8259139.
- Remuzzi G, Schieppati A, Ruggenenti P. Nephropathy in patients with type 2 diabetes. The New England Journal of Medicine. 2002; 346:1145-51. Crossref. PMid:11948275
- Gross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T. Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care. 2005; 28(1):176-88.Crossref.
- Miller RA. Medical diagnostic decision support systemspast, present, and future: A threaded bibliography and brief commentary. Journal of the American Medical Informatics Association. 1994; 1(1):8-27. Crossref. PMid:7719792 PMCid:PMC116181.
- Alonso-Amo F, Perez AG, Gomez GL, Montes C. An expert system for homeopathic glaucoma treatment (SEHO). Expert Systems with Applications. 1995; 8(1):89-99. Crossref.
- Singla J, Jindal N. The diagnosis of some tweens childhood diseases in a prolog expert system. Proceedings of National Conference on Advances in Engineering and Technology. 2014; p. 1-4. PMid:26328137 PMCid:PMC4548645.
- Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England. 2004; 86(5):334-8. Crossref. PMid:15333167 PMCid:PMC1964229.
- Roventa E, Rosu G. The diagnosis of some kidney diseases in a small prolog expert system. Proceedings of the 3rd International Workshop on Soft Computing Applications. 2009; p. 219-24. Crossref.
- Singla J. The diagnosis of some lung diseases in a prolog expert system. International Journal of Computer Applications. 2013; 78(15):37-40. Crossref
- Singla J. Comparative study of mamdani-type and sugenotype fuzzy inference systems for diagnosis of diabetes. Proceedings of International Conference of Advances inComputer Engineering and Applications. 2015; p. 517-22.Crossref.
- Bhandari V, Kumar R. Comparative analysis of fuzzy expert systems for diabetic diagnosis. International Journal of Computer Applications. 2015; 132(6):8-14. Crossref.
- Novak V. Which logic is the real fuzzy logic? Fuzzy Sets and Systems. 2006 Mar; 157(5):635-41. Crossref, Crossref.
- Sproule BA, Naranjo CA, Turksen IB. Fuzzy pharmacology: Theory and applications. Trends in Pharmacological Sciences. 2002 Sep; 23(9):412-7. Crossref.
- Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences. 1975; 8(3):199-249. Crossref
- Kaur A. Comparison of mamdani-type and sugenotype fuzzy inference systems for air conditioning system. International Journal of Soft Computing and Engineering. 2012 Jan; 2(2):323-5.
- Kamboj V, Kaur V. Comparison of constant sugeno-type and mamdani-type fuzzy inference system for load sensor. International Journal of Soft Computing and Engineering. 2013; 3(2):1-4.
- Arora M, Tagra D. Neuro-fuzzy expert system for breast cancer diagnosis. Proceedings of International Conference on Advances in Computing, Communications and Informatics. 2012; p. 979-85. Crossref.
- Devi ER, Nagaveni N. Design methodology of a fuzzy knowledgebase system to predict the risk of diabetic nephropathy. International Journal of Computer Science Issues. 2010; 7(5):1-9.
- Narasimhan B, Malathi A. Fuzzy logic system for risklevel classification of diabetic nephropathy. Proceedings of International Conference on Green Computing, Communication and Electrical Engineering. 2014; p. 1-4. Crossref.
- Meza-Palacios R, Aguilar-Lasserre AA, Ure-a-Bogarin EL, Vazquez-Rodriguez CF, Posada-Gomez R, Trujillo-Mata A. Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus. Expert Systems with Applications. 2017 April; 72:335-43. Crossref.
- Kaur A. Comparison of fuzzy logic and neuro fuzzy algorithms for air conditioning system. International Journal of Soft Computing and Engineering. 2012; 2(1):417-20.
- Thakur M, Kaur A. Neuro-fuzzy based fake currency detection system. International Journal of Advanced Research in Computer Science and Software Engineering. 2014; 4(7):1-8.
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