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  Implementation of Neuro Fuzzy System for Diagnosis of Multiple Sclerosis  
  Authors : Mohammad Esmaeil Shaabani; Touraj Banirostam; Alireza Hedayati
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Medical diagnosis is often done by expertise and experience of phisician, but sometimes may lead to misdiagnosis. Multiple sclerosis (MS) is a disease of the central nervous system. In this disease, body produces antibodies that attack and damage the Myelin, in which the myelin sheath (the insulation for nerve fibers) is in trouble and the damage to myelin in the central nervous system cause to disconnect between brain and other organs. The major problem is the lack of diagnosis. To improve diagnosis, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. ANFIS main idea is that using the way the nervous system of biological for data processing in order to learn and create the knowledge. This system uses neural network for learning, classification capabilities and modifying. There are several ways to train neural network. In this study, we use hybrid approach to train. This hybrid approach uses Back Propagation(BP) and Least Square Error(LSE). ANFIS has the ability to combine the linguistic power of fuzzy system with numeric power of neural network. For optimizing the input/output, the K-fold cross validation has been used. Implementation has been done in MATLAB. The Data set consist of 600 patients that each one has 6 columns, 5 of them is input and 1 of them is output that shows diagnosis.


Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 157-164

Figures :12

Tables : --

Publication Link : Implementation of Neuro Fuzzy System for Diagnosis of Multiple Sclerosis




Mohammad Esmaeil Shaabani : Department of Computer Engineering, Tehran Center Branch, Islamic Azad University Tehran, Iran

Touraj Banirostam : Department of Computer Engineering, Tehran Center Branch, Islamic Azad University Tehran, Iran

Alireza Hedayati : Department of Computer Engineering, Tehran Center Branch, Islamic Azad University Tehran, Iran








Multiple Sclerosis

Fuzzy System

Neural Network


Hybrid Learning

Decision support system play an important role in patient care. False detection in each kind of diseases have an irreparable damage to patients and clinican. The aim of this study is improve diagnosis of MS. We had tried to bring the learning ability of neural network into fuzzy inference system to improve the diagnosis of MS. We use 600 patient's data consist of 5 features and use hybrid learning algorithm in ANFIS that applied Least Square Estimation (LSE) and Back Propagation (BP) to reduce the diagnosis error. For optimize the input data and evaluate the performance of our system we use K-fold cross validation. Proposed system compared with ANFIS with BP algorithm and GRNN. Simulation result show that proposed system has almost 96% accuracy.










[1] Smelterz. S. , brandgi, B. , Heinkel, J. , chioyer , K. , "Neurology" , Tehran, Year: 2010. [2] Soltanzadeh, A. ,," Neurological and muscles disease" ,Tehran, Year:2004. [3] Antoni, F. , Harrison, T. , Department, University, City, "Neurological disease", Tehran, Year: 2009. [4] Tahami, E. , Bamshaki, M. , Khalilzadeh, M. "Diagnosis of diabetes type 1 using a combination of ANFIS an GA-NN", 8th conferance on artifitial systems, Ferdosi university of mashhad, Year: 2007. [5] Masroor, M. , " MS lesions on MRI image segmentation " , Electrical Engineering and Sustainable Development ,Tehran,Year:2010. [6] Tabrizi, N. , Eatemadifar , M. , Sharifi, E. , Mirmehdi, R. , "Multiple Sclerosis" , Tehran , Year:2012. [7] Akbari, M. , " Providing a fuzzy decision support system to assess organizational readiness for adoption of knowledge management " , Journal of Management Technology Development ,Year: 2013. [8] Zahedi, F. , " A review of fuzzy neural-based intelligent control ", Electrical Engineering and Sustainable Development ,Tehran,Year:2012. [9] Sabziparvar, A. , Bayatvarkeshi, M. , " Evaluate the accuracy of artificial neural networks and neural - fuzzy simulated solar radiation ", Iranian phisics research, Tehran , Year: 2009. [10] Ramzanian, M. , " New approaches to predict oil prices using Fuzzy Neural Networks ", journal of Management studies in Iran, Tehran, Year: 2013. [11] Abedini, M. , moatamedinasab, F. , "MS and complementary medicine" , sari, Year: 2012. [12] Menhaj, M. , " Principles of Neural Networks (Artificial Intelligence)" , Tehran ,Year:2009. [13] Davarpanah, H. , Mirzaei, A. ,"Artificial intelligence" , Mashhad, Year: 2005. [14] Kia, M. , "Designing neural networks", Tehran, Year: 2011. [15] Esposito, M. , DePietro, G. , "An ontology-based fuzzy decision support system for multiple sclerosis" , international conference on Engineering Applications of Artificial Intelligence Volume 24, Year:2011, pages: 1340–1354 [16] Esposito,M. , De Falco,I. , De PietroTG. ," An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease". international journal of medical informatics, Volume 80, Issue 12,Y:2011 , pp:e245–e254. [17] Pombo, N. , Araújob,P. , Vianac,J. , "Knowledge discovery in clinical decision support systems for pain management: A systematic review" . Artificial Intelligence in Medicine, volume:60 , Issue : 1, Y:2014 ,pp: 1– 11 [18] Aydogan,E., Karaoglan,I. ,Pardalos,P. , “ hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems “. Applied Soft Computing ,volume 12, Issue: 2 , Y:2012, pp:800–806. [19] W.L. Tung, C. Quek. "GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data". Artificial Intelligence in Medicine,volume:33 , Issue: 1, Y:2005, pp:61-88. [20] Ghasemi,J. ,Ghaderi,R. , Karami Mollaei,M.R. , Hojjatoleslami,S.A. , "A novel fuzzy Dempster–Shafer inference system for brain MRI Segmentation. ",Information Sciences, Volume:223, Y: 2013 , pp:205–220. [21] García-Lorenzo,D. ,Francis,S. ,Narayanan,S. , L. Arnold,D. , Louis Collins,D. , " Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging " , information sience international conference on Medical Image Analysis, Volume :17, Issue:1,Year: 2012, Pages:1–18. [22] Khotanlou,H. , Afrasiabi,M. , " Segmentation of Multiple Sclerosis Lesions in Brain MR Images using Spatially Constrained Possibilistic Fuzzy C-means Classification." Journal of Medical Signals & Sensors, Vol 1, Issue 3, Y:2011 , PP:149-155. [23] Hazlina Hamdan and Jonathan M. Garibaldi, " Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival" , WCCI 2010 IEEE World Congress on Computational Intelligence July, 18-23, CCIB, Barcelona, Spain , Year: 2010, pages : 1 – 8. [24] Nopparatkiat,P. , na Nagara,B. , Chansa-ngavej,C. , " Expert system knowledge acquisition for melasma skin diagnosis and treatment with Thai herbal medicine." , Int. Journal of Applied Sciences and Engineering ,VOL 1 ,Issue 5,Y: 2012. [25] Borgohain,R. , Sanyal,S. , "Rule Based Expert System for Diagnosis of Neuromuscular Disorders".Int J.Advanced Networking and pplications ,Volume:34 ,Issue:31 ,Pages:1509-1513 (2012) [26] Agboizebeta, I.A & Chukwuyeni, O.J. , " Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means" , International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.1, January 2012 [27] Masudur,S.M. ,Al-Arif,R. , Quader,N. , Shaon,A.M. , Khairul,K. , " Sensor based Autonomous Medical Nanorobots. A cure to Demyelination." , Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Nanotechnology (JSAN), Y:2011. [28] Mangalampalli, A. , Mangalampalli, S.M. , Chakravarthy, R. ,Jain, A.K. , " A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system" , conference on Expert Systems with Applications , vol:30, Year:2006 , pp:109–116 [29] Douali,N. , Csaba,H. , De Roo,J. , Papageorgiouc,E.I. , Jaulent,M.C. , " Diagnosis Support System based on clinical guidelines: comparison between Case-Based FuzzyCognitive Maps and Bayesian Networks" , conference on computer methods and programs in biomedicine, vol:113 ,Issue:1, Year:2014, pp:133-143. [30] Agharezaei,Z. , Bahaadinbeigyb,K. , Tofighi,Sh. , Agharezaei,L. , Nemati,A. ; "Attitude of Iranian Physicians and Nurses toward a Clinical Decision Support System for Pulmonary Embolism and Deep Vein Thrombosis". Conference on Computer Methods and Programs in Biomedicine , Volume:115, Year:2014, pages : 95-101. [31] P.K. Anooj , "Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules" . Journal of King Saud University – Computer and Information Sciences ,volume: 24, Year:2012,Pages: 27–40 [32] Shiee, N. , ,Bazin, P. ,Ozturk, A. ,Reich, S., Calabresi, P. ,Pham, D., "A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions".NeuroImage ,volume:49,issue 2, Y:2010 , pp:1524–1535. [33] Shah,M. , Xiao,Y. ,Subbanna,N., Francis,S. , L. Arnold,D. ,Collins,D.L. ,Arbel,T. ,"Evaluating intensity normalization on MRIs of human brain with multiple sclerosis ". Medical Image Analysis 15 (2011) 267–282. [34] Datta,S. , Narayana,P.A. , "A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis". NeuroImage: Clinical ,volume: 2, Y:2013, pp: 184–196. [35] Arabzadeh Ghahazi, M. , Fazel Zarandi, M.H. , Rahimi Damirchi-Darasi,S. , Harirchian, M. H. , "Fuzzy Rule based Expert System for Diagnosis of Multiple Sclerosis" , IEEE Conference on Norbert Wiener in the 21st Century (21CW),Year: 2014 , pages: 1 – 5 .[36] Dr.C.Loganathan and 2 K.V.Girija , " Hybrid Learning For Adaptive Neuro Fuzzy Inference System ", International Journal Of Engineering And Science, Vol.2, Issue 11,Year:2013, Pages: 6-13. [37] Abdullah, Bassem A., "Segmentation of Multiple Sclerosis Lesions in Brain MRI" (2012). Open Access Dissertations. Paper 111. [38] Llado,X. ,Oliver,A. ,Cabecas,M. , Freixeneta,J. , Vilanovab,J.C. , Quilesc,A. , Vallsc,L. ,Ramió- Torrentàd,L. , Rovirae,A. , " Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches " ,information sience, volume: 186 ,Issue 1, Y: 2012, PP:164-185. [39] El-Sappagh,S.H. ,El-Masri ,S. , " A distributed clinical decision support system architecture". Journal of King Saud University - Computer and Information Sciences , Volume 26, Issue 1, Y:2014,PP:69-78. [40] Sheng-Ta Hsiehl & ehun-Ling Lin, "Work-In-Progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system" , 1st International Conference on Industrial Networks and Intelligent Systems (INISCom) , Year: 2015, pages: 177 – 180. [41] Timothy J. Ross."FUZZY LOGIC WITH ENGINEERING APPLICATIONS”. Second Edition.University of New Mexico, USA,Y:2004. [42] Nguyen,T. , Khosravi,,A. , Creighton,D. ,Nahavandi,S. , "Medical Diagnosis by Fuzzy Standard AdditiveModel with Wavelets" , IEEE International Conference on Fuzzy Systems (FUZZIEEE) July 6-11, 2014, Beijing, China. , Year: 2014, pages: 1937 – 1944. [43] Boyacioglua,M.A. ,Avcib,D. ,." An Adaptive Network- Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange" , Expert Systems with Applications, Volume 37, Issue 12, December 2010, Pages 7908–7912. [44] José Hernández-Orallo. "ROC curves for regression", Pattern Recognition, Volume 46, Issue 12, December 2013, Pages 3395-3411. [45] Vijay Kumar Garg & Dr. R.K. Bansal , "Soft Computing Technique Based on ANFIS for the Early Detection of Sleep Disorders", IEEE International Conference on Advances in Computer Engineering & Applications (ICACEA-2015), Year: 2015, pages : 76 – 79 . [46] Güvenç,S.A , Demir,M. , Ulutas,M. , "Detection Of Forearm Movements Using Wavelets And Adaptive Neuro-Fuzzy Inference System (ANFIS)" , IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, Year:2014 , pages : 192 - 196. [47] Mahmoudi, S. , Sadeghi Lahijan,B. , Rashidy Kanan,H. , "ANFIS-Based Wrapper Model Gene Selection for Cancer Classification on Microarray Gene Expression Data" , IEEE ,13th Iranian Conference on Fuzzy Systems (IFSC) , Year:2013 , pages: 1 - 6 . [48] Kalaiselvi, C. & Dr. G .M. Nasira, "A New Approach for Diagnosis of Diabetes and Prediction of Cancer using ANFIS", IEEE World Congress on Computing and Communication Technologies,Year:2014, pages: 188 – 190. [49] Mohammad A. M. Abushariah, Assal A. M. Alqudah, Omar Y. Adwan, Rana M. M. Yousef, " Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches " , Journal of Software Engineering and Applications Vol.7 No.12,Year: 2014. [50] Garg, V.K.; Bansal, R.K. "Soft Computing Technique Based on ANFIS for the Early Detection of Sleep Disorders" , International Conference on Advances in Computer Engineering and Applications (ICACEA), Year:2015 , Pages:76-79. [51] Sheng-Ta Hsieh; Chun-Ling Lin , " Work-In-Progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system" , 1st International Conference on Industrial Networks and Intelligent Systems (INISCom), Year:2015 , Pages:177-180. [52] Vyas, D.; Misra, Y.; Kamath, H.R. , "Comparison and Analysis of Defuzzification Methods of a Fuzzy Controller to Maintain The Cane Level During Cane Juice Extraction" , International Conference on Signal Processing And Communication Engineering Systems (SPACES), Year: 2015 , Pages:102-106. [53] Bhuvaneswari Amma N G, " An Intelligent Approach Based on Principal Component Analysis and Adaptive Neuro Fuzzy Inference System for Predicting the Risk of Cardiovascular Diseases" , Fifth International Conference on Advanced Computing, IEEE 2013, Pages: 241-245. [54] Bhardwaj, S.; Singhal, N.; Gupta, N. " Adaptive Neurofuzzy System for Brain Tumor",IEEE International Conference on Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity,Year: 2014 , Pages:1-4.