International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN

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1 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN COMPARISON OF MAMDANI, SUGENO AND NEURO FUZZY MODELS FOR DIAGNOSIS OF DIABETIC NEPHROPATHY Jimmy Singla 1, Dinesh Grover 2 1 Research Scholar, Faculty of Engineering, IKGPTU Kapurthala 2 Ex-Director, Dept. of CSE & IT, LLRIET Moga ABSTRACT: Medical expert system for diagnosis of nephropathy is developed using mamdani, sugeno and neuro fuzzy models. It is seven input one output system where the inputs are GFR, Blood Glucose, T2DMA, Uric Acid, Serum Creatinine, Hypertension, Dyslipideia and output is control of nephropathy. All the three models are simulated in MATLAB and their results are compared. Keywords: mamdani, sugeno, neuro fuzzy, nephropathy, diagnosis [1] INTRODUCTION There is a trouble of diabetes in developing countries. The major number of diabetics in the globe is present in India with frequency of 11.8% in urban and 3.8% in rural adults. Both the type 1 and type 2 diabetes direct to end stage renal disease. The reason behind this is the delayed detection of nephropathy in patients having diabetes. Nephropathy can be controlled by early recognition and treatment of renal changes 1, 2. In early detection of nephropathy, the doctor may recommend the appropriate measures to decrease the risk of nephropathy, he may accept the multifactorial interventions and apply the agents with renoprotective effect. So the early detection of diabetic nephropathy is necessity for the longer survival of patients. But due to ambiguous information, the diagnosis of disease is overwhelming task in some cases 3, 4. For example some symptoms lead to different interpretations. The diagnosis in these cases is quite Jimmy Singla and Dinesh Grover 11

2 COMPARISON OF MAMDANI, SUGENO AND NEURO FUZZY MODELS FOR DIAGNOSIS OF DIABETIC NEPHROPATHY complicated. Hence the expert also needs some help to make the right diagnosis 5. In some cases, some symptoms of the disease are very much common and some symptoms are very alike. It again produces difficulty for the expert to make the diagnosis 9. The medical expert systems are built to support the doctors to arrive at the right diagnosis 6. The medical expert systems are made up of programs and medical knowledge base. The knowledge about the infection is available in the knowledge base 7. In simple rule based medical expert systems, the user is asked to respond yes or no if a particular symptom arises or not. In the end, on account of user s responses name of the disease is found out 8. Fuzzy medical expert systems are grouping of rules and membership functions. Fuzzy systems are sloping towards mathematical processing 10. Fuzzy logic is an emergent tool for its modeling using real values taken from structured range. It is likely to retain as many features of classical logic as feasible 11. Fuzzy logic is a data processing methodology that is extremely advisable when trying to model indefinite information and to formulate rational decisions in an uncertainty environment 12. The Mamdani fuzzy model is among the first control systems based on fuzzy logic. Mamdani's efforts are based on the work of Lotfi A. Zadeh on fuzzy algorithms for complex systems and decision processes. This method is widely recommended as expert method for mastering fuzzy logic. The output from Mamdani fuzzy system can be easily transformed to a linguistic form as the inference result before defuzzification. Mamdani fuzzy system is widely used in particular for decision support application. However, this system entails a substantial computational burden. On the other hand, Sugeno method is computationally efficient and works well with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for dynamic non-linear systems. The main difference between Mamdani and Sugeno model resides in the way the crisp output is generated from the fuzzy inputs. While Mamdani model uses the technique of defuzzification of a fuzzy output, Sugeno model uses weighted average to compute the crisp output, so the Sugeno s output membership functions are either linear or constant but Mamdani s inference expects the output membership functions to be fuzzy sets. Furthermore, Sugeno method has better processing time since the weighted average replace the time consuming defuzzification process. Neuro-fuzzy system combines the learning capabilities of the neural networks and the control capabilities of a fuzzy logic system. It is a system that uses a learning algorithm to determine its parameters by processing data samples. [2] BACKGROUND WORK Classification system diagnoses several clinical problems. Diabetic nephropathy is one of them. There are a number of studies for the diagnosis of diabetic nephropathy. Rama Devi has developed fuzzy knowledge based system to categorize the threat of diabetic nephropathy. This structure assists to determine the renal failure and protects the patient from ESRD. Narasimhan planned fuzzy logic system for the diabetic nephropathy control and achieved different parameters like classification accuracy, sensitivity and specificity. Meza-Palacios proposed a fuzzy inference system for the evaluation of diabetic nephropathy. This fuzzy inference system succeeds in up to 93.33% of the cases. Jimmy Singla and Dinesh Grover 12

3 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN [3] DEVELOPMENT OF MAMDANI FUZZY MODEL Medical expert system for diagnosis of nephropathy is first developed using mamdani fuzzy model. It consists of seven inputs and one output. The inputs are glomerular filtration rate (GFR), blood glucose, type 2 diabetes mellitus age (T2DMA), uric acid, serum creatinine, hypertension and dyslipedemia. The output is control of nephropathy. The range of all inputs and output is shown in table I. TABLE I. Range of Inputs & Output S. No. Input/Output Range 1 GFR 0 to Blood Glucose 0 to T2DMA 0 to 30 4 UricAcid 0 to 10 5 Serum Creatinine 0 to 2 6 Hypertension 0 to 1 7 Dyslipedemia 0 to 1 8 Nephropathy Control 0 to 4 The membership functions for all the inputs and output are shown in figure 1 to 8. Rules, structure and rule viewer are shown in figure 9, 10 and 11 respectively. Fig. 1: Membership functions for GFR Fig. 2: Membership functions for Blood Glucose Fig. 3: Membership functins for T2DMA Fig. 4: Membership functions for Uric Acid Jimmy Singla and Dinesh Grover 13

4 COMPARISON OF MAMDANI, SUGENO AND NEURO FUZZY MODELS FOR DIAGNOSIS OF DIABETIC NEPHROPATHY Fig. 5: Membership functions for Serum Creatinine Fig. 6: Membership functions for Hypertension Fig. 7: Membership functions for Dyslipidemia Fig. 8: Membership functions for Nephropathy control Fig. 9: Rules for the Mamdani model Fig. 10: Structure of the Mamdani model Jimmy Singla and Dinesh Grover 14

5 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN Fig. 11: Rule Viewer for the Mamdani model [4] DEVELOPMENT OF SUGENO FUZZY MODEL For development of medical expert system for diagnosis of nephropathy using sugeno model, the beginning steps are same as in mamdani model. It also gets inputs from GFR, blood glucose, T2DMA, uric acid, serum creatinine, hypertension and dyslipidemia providing all the symptoms of a patient. It produces output that tells control of nephropathy. Here in sugeno model, output can be either constant or linear. So four membership functions for the output are severe, moderate, minor lack of control and good control which are constant and shown in table 2. The output using sugeno model can only be in range of 0 to 1. The fuzzy rule base for sugeno model is same as of mamdani model. Rule viewer and structure for sugeno model is shown in figure 12 and 13 respectively. TABLE II. Nephropathy control with constant value S. No. Constant Nephropathy Control Value 1 0 Severe lack of control Moderate lack of control Minor lack of control 4 1 Good control Fig. 12: Structure of the Sugeno model Fig. 13: Rule Viewer for the Sugeno model Jimmy Singla and Dinesh Grover 15

6 COMPARISON OF MAMDANI, SUGENO AND NEURO FUZZY MODELS FOR DIAGNOSIS OF DIABETIC NEPHROPATHY [5] DEVELOPMENT OF NEURO FUZZY MODEL Neuro-fuzzy system for diagnosis of diabetic nephropathy is developed using ANFIS. Data is collected from doctors in medical evaluations on patients of diabetic nephropathy. Fuzzy expert system is generated through seven inputs plus one output. The seven inputs get the name input1, input2, input3, input4, input5, input6, input7 respectively and the output gets the name output. Input1 to input3 each is having three Gaussian membership functions and input4 to input7 each is having two Gaussian membership functions. The generated fuzzy expert system is then trained for the input output data bank collected from experts. The membership functions of input1 to input 7 with their range are shown in figure 14 to figure 20. Rule viewer and rules are shown in figure 21 to 23. Training of data is shown in figure 24. Fig. 14: Membership functions for input1 Fig. 15: Membership functions for input2 Fig. 16: Membership functions for input3 Fig. 17: Membership functions for input4 Jimmy Singla and Dinesh Grover 16

7 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN Fig. 18: Membership functions for input5 Fig. 19: Membership functions for input6 Fig. 20: Membership functions for input7 Fig. 21: Rule viewer Fig. 22: Rules Jimmy Singla and Dinesh Grover 17

8 COMPARISON OF MAMDANI, SUGENO AND NEURO FUZZY MODELS FOR DIAGNOSIS OF DIABETIC NEPHROPATHY Fig. 23: Rules Fig. 24: Training at 30 epochs [6] RESULTS In the course of testing the performance of the models, the doctors categorize acceptably and wrongly diagnosed patient cases by comparing the results given by the system with that of the doctors judgments reached on the same patient test cases. Performance of the system is usually estimated using the statistics in the matrix. The following tables III, IV and V illustrate the confusion matrix for the four class classifier using mamdani, sugeno and neuro fuzzy model respectively. TABLE III. Confusion matrix array using Mamdani fuzzy model Severe Moderate Minor Good Class Names Severe Moderate Minor Good TABLE IV. Confusion matrix array using Sugeno fuzzy model Severe Moderate Minor Good Class Names Severe Moderate Minor Good TABLE V. Confusion matrix array using Neuro fuzzy model Severe Moderate Minor Good Class Names Severe Moderate Minor Good Jimmy Singla and Dinesh Grover 18

9 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN Further specificity, sensitivity, precision and accuracy are calculated based on these confusion matrices. These are illustrated in table VI and VII. TABLE VI. Performance parameters S. No. Model Sensitivity Specificity (%) (%) 1 Mamdani Sugeno Neuro fuzzy TABLE VII. Performance parameters S. No. Model Precision (%) Accuracy (%) 1 Mamdani Sugeno Neuro fuzzy [7] CONCLUSION From this paper, it is concluded that for diagnosis of nephropathy, the output achieved from using neuro fuzzy model is more accurate as compared to mamdani and sugeno models. [8] ACKNOWLEDGMENT Jimmy Singla, Author would like to thank his supervisor, Prof. Dinesh Grover for the guidance and I. K. Gujral Punjab Technical University for the facilities provided throughout the research work. REFERENCES 1. Bojestig M, Arnqvist HJ, Hermanson G, Karlberg, BE, LudvigssonJ. Declining incidence of nephropathy in insulin-dependent diabetes mellits. The New England Journal of Medicine. 1994, 330(1), pp Remuzzi G, Schieppati A, Ruggenenti P. Nephropathy in patients with type 2 diabetes. The New England Journal of Medicine. 2002, 346, pp Gross JL, de Azevedo MJ, Silveiro SP, Canani, LH, Caramori ML, Zelmanovitz T. Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care. 2005, 28(1), pp Miller RA. (1994) Medical diagnostic decision support systems-past, present, and future: A threaded bibliography and brief commentary. Journal of the American Medical Informatics Association. 1994, 1(1), pp Alonso-Amo F, Pérez AG, Gómez GL, Montes C. An expert system for homeopathic glaucoma treatment (SEHO). Expert Systems with Applications. 1995, 8(1), pp Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England. 2004, 86(5), pp 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, pp Singla J. The diagnosis of some lung diseases in a prolog expert system. International Journal of Computer Applications. 2013, 78(15), 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, pp Jimmy Singla and Dinesh Grover 19

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