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.
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
ANFIS
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.