Adaptive Neuro Fuzzy Inference System (ANFIS): MATLAB Simulation of Breast Cancer Experimental Data

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1 IOSR Journal of Computer Engneerng (IOSR-JCE) e-issn: ,p-ISSN: , Volume 19, Issue 4, Ver. V. (Jul.-Aug. 2017), PP Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer Expermental Data Ejofor C. I 1 & Okengwu U.A 2. Department of Computer Scence, Unversty of Port-Harcourt, Port-Harcourt. Ngera 1,2. Correspondng Author: Ejofor C. I Abstract: The accurate predcton of breast cancer has been an area of nterest due to the complextes assocated wth expermental data for breast cancer. Ths paper has decded to explore the Adaptve Neuro Fuzzy Inference system (ANFIS) usng Matrx Laboratory (MATLAB) for the smulaton of Wnconsn breast cancer expermental data. Twenty (20) Wnconsn breast cancer dataset was used for the smulaton, comprsng of nne (09) attrbutes wth dagnosng values dentfyng Malgnant or Bengn. MATLAB ANFIS smulaton captured the fundamental edtors nclusve of tranng, testng and ANFIS structure. The result of smulaton showed that from all parameters of smulaton; numbers of nodes had the hghest values followed by total number of parameters, number of parameters, number of non-lnear parameters and number of tranng data respectvely. Keyword: ANFIS, Breast Cancer, Smulaton, Expermental data (dataset) Date of Submsson: Date of acceptance: I. Introducton Breast tumour s a generc name for malgnancy and non-malgnancy; wth malgnancy accommodatng the propensty for propagaton but lackng hyperplasa whle non-malgnancy lack the propensty for propagaton but accommodatng hyperplasa. Breast cancer; a form of tumour usually starts off wthn the nner lnng of mlk ducts or the lobules whch supply mlk for nursng mothers wthn the breast. Breast cancer orgnatng from lobules s usually known as lobular carcnoma whle one that develops from the ducts s called ductal carcnoma. Breast cancer undenably s the most common cancer among female worldwde whch account for 16% of all female cancer and 18.2 of all cancer death worldwde (MedcneNet, 2016). Breast cancer s perceved as a malgnant dsease caused by uncontrolled growth of abnormal cells n the breast (WrongDagnoss, 2011). These abnormal cells (old or damaged) are created for destructon or death after cell dvson wth the am of preventng cancerous growth. The statstcal occurrence of cancerous cell n female breast s more common for caucasan women as opposed to Afrcan-Amercan women. Usually, the rsk factor for breast cancer ncludes beng over 50 years of age, havng a personal or famly hstory of breast cancer. Other factor may nclude consumng hormones estrogen, startng menstruaton before 12 year of age (early menstruaton), startng menopause after age 55 and gvng brth after 30 (late chldbearng). Radaton may also pay an mportant role n obtanng cancerous growth, f expose repeated to radaton elements. These rsk factors left unchecked wll result n breast cancer, whle breast cancer left untreated, multply, spread and propagate (Oncolex, 2016). A dagnoss of breast cancer can easly be delayed or mssed due to mnute breast lump affectng a mammogram, or lump may not be panful and mght be gnored. In addton, some symptoms of breast cancer can resemble symptoms of other dseases and condtons. The treatment of breast cancer vares, dependng on the ndvdual case and the type and stage of the cancer. Treatment optons may nclude surgery, radaton therapy, and chemotherapy (MedcneNet, 2016; Oncolex, 2016 and WrongDagnoss, 2011). However, early dagnoss s the fundamental key for prompt treatment of breast cancer whch fosters huge success. Overtme, the complextes of breast cancer have been overwhelmng. These complextes usually are sponsored by late detecton of the dsease, usually when several vtal organs have been comprsed due to contnuous and actve propagaton. Another notable ssue nvolves the accurate classfcaton of breast cancer; dfferentatng between bengn and malgnancy cancerous growth. These two fundamental ssues (promptness and accuracy) requre novel methods n enhancng dagnoss (CancerQuest, 2016). Tll date, methods for the accurate and early detecton of breast cancer are of utmost mportance and are stll an actve area of current research (CancerQuest, 2016). Standng the aforementoned, ths paper ntends to apply ANFIS MATLAB smulaton for breast. DOI: / Page

2 Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. II. Appled Materal A Neuro-Fuzzy approach; Adaptve Neuro Fuzzy Inference System (ANFIS) s a combnaton of an Artfcal Neural Network (ANN) and a Fuzzy Inference System (FIS) usng the Takag Sugeno Model and adaptve neural network learnng approach capabltes. Fgure 1 depcts the ANFIS model Fgure 1: Structure of the ANFIS Network. ANFIS s a sx layers network (read as 1 6 or 0-5), wth dstnct functonal nodes performng specfc functon. The learnng or tranng of ANFIS s structured usng a hybrd approach, combnng the Backward- Propagaton Gradent Descent (BPGD) and a Least-Squares Estmator (LSE) method. Ths structure possesses computatonal capablty of neural network wth the explanatory power of fuzzy logc. The model comprses of sx ntegrate layers, whch ncludes: a. Layer 0 (L0): The Input layer s the frst ANFIS archtecture layer. Ths layer conssts of nput nodes whch represents the nput parameters. b. Layer 1 (L1): Ths layer s called the membershp functon layer. The functon of each node n ths layer s maps the nput of the lngustc varable from the nput layer to varable lngustc values usng an approprate membershp functon. c. Layer 2 (L2): Ths layer s called the rule layer. The node n ths layer receves nput from the membershp functon layer and calculates the frng strength of each rule usng the mn or prod operator. d. Layer 3 (L3): Ths layer s called the normalzaton layer and the node n ths layer receves nput from the rule layer, computes the rato of the ncomng sgnal from the rule layer and t computes the output and the output of ths layer. e. Layer 4 (L4): Ths layer s known as the defuzzfcaton. The nodes compute a parameter functon on the normalzaton layer. Parameters n ths layer are called consequent parameters. f. Layer 5 (L5): Ths s known as the output layer. It comprses of a sngle node whch sums all the ncomng sgnals and produces the output of the ANFIS system ANFIS: as a predctve model learns from a set of expermental data whch are usually structured to a partcular doman problem. Ths doman usually possesses parameter nput and rules whch are obtaned from the expermental data. The antecedent parameters (ANFIS nput) are obtaned from the expermental data nput fuzzfed wth an approprate membershp functon. The consequent parameters are repeated obtaned by adjustng the antecedent parameters utlzng the hybrd algorthm (Least Square Estmator: LSE and Back Propagaton Gradent Descent: BPGD). Durng the tranng process, the expermental data s fed nto the ANFIS model n cyclcal form seen an epoch; comprses of both the forward pass (LSE) and the Backward Pass (BPGD). The adjustment of premse parameters and consequent parameters are captured as an epoch. III. Methodology: Breast Cancer Predcton usng Adapted Neuro-Fuzzy Inference System (BC- ANFIS). The adopted approach mplement breast Cancer predcton usng ANFIS. The selected Wsconsn Breast Cancer Dataset obtaned from UCI Machne Learnng Repostory was used for smulaton ( (orgnal). The expermental data comprses of thrteen (13) columns. These columns ncludes cases, expermental code, nne (09) decson varables, dagnoss values (2, 4) and class of classfcaton (Malgnant or Bengn). These columns cut-across 20 selected cases. Table 1 shows these expermental data. DOI: / Page

3 Cases Dataset Code Clump Thckness Unformty of Cell Sze Unformty of Cell Shape Margnal Adheson Sngle Epthelal Cell Sze Bare Nucle Bland Chromatn Normal Nucleol Mtoses Classes of Dagnoss (2- Bengn, 4-Malgnant Classfcaton class Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. Table 1: Wsconsn Breast Cancer Dataset Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant The expermental data (Fgure 3.1) served as the nput base for BC-ANFIS. Ths model s an adopted ANFIS approach comprses of preprocessng, nput, fuzzfcaton, rule, normalzaton, defuzzfcaton, (tranng; LSE/BPGD) output. Fgure 2 depcts the BC-ANFIS. Fgure 2: BC-ANFIS Model. The BA-ANFIS model comprses of two man modules; Preprocessor block and the ANFIS block. a. Preprocessor Block: The preprocessor block provdes an avenue for preprocessng the crsp values (x 1) nto fuzzfed noseless expermental data (x < 1) pror to tranng. The preprocessng of the expermental data (Table 1) was handled usng the equaton 1 wth dagnoss value the joggng up. Table 2 shows the fuzzfed noseless expermental data. DOI: / Page

4 Cases Dataset Code Clump Thckness Unformty of Cell Sze Unformty of Cell Shape Margnal Adheson Sngle Epthelal Cell Sze Bare Nucle Bland Chromatn Normal Nucleol Mtoses Classes of Dagnoss (2-Bengn, 4-Malgnant Classfcaton class Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. Table 2: Preprocessed Fuzzfed Wsconsn Breast Cancer Dataset Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant Bengn Malgnant b. ANFIS Block: The ANFIS block comprses of sx man layers. These layers nclude. Input Layer: Layer 1 (nput layer) accept and transt the parameters of breast cancer ted to ther assocated values to next successve layer. Membershp Functon Layer: Layer 2 (membershp functon) map varous membershp functon to each nput lngustc varables. The BC-ANFIS utlzed Bell membershp functon due to the propensty for huge dataset. Equaton 1 depcts the Bell Membershp Functon A ( x) 1 1 x c b [ ( ) 2 ] 2 a... Equaton 2 where {a, b, c} are called premse parameters.. Rule Layer: Layer 3 (Rule layer) receves fuzzfed range value from the nput layer and calculates the frng strength of each rule. Equaton 3 depcts the frng ANFIS strength. O2, w A ( x) B ( x) 1, 2... Equaton 3 v. Normalzaton Layer: Layer 4 (Normalzaton layer) computes the normalzed frng strength of each nput sgnal comng from the rule layer. Equaton 4 depcts the normalzaton frng strength. w O3, w 1, 2... Equaton 4 w1 w2 v. Defuzzfcaton Layer: Layer 5 (Defuzzfcaton layer) compute a parameter functon on the normalzed output formng the consequent parameters. Equaton 5 hghlght the defuzzfcaton frng strength O4, w f w ( p x q y r ) { p, q, t }... Equaton 5 v. Output Layer: Layer 5 (Output layer) computes the overall values. Ths overall output s mapped to one of the two value bengn or malgnant. The overall output s hghlghted n Equaton 6 O5, 1 overall output w f... Equaton 6 IV. Matrx Laboratory (MATLAB) Smulaton The BC-ANFIS was smulated usng MATLAB takng cognzant of numercal data ntegraton and open source capablty of matlab. Seventy-fve (75%: 15 cases) of expermental data was used for tranng whle Twenty-Fve (25%: 5 cases) was used for testng. Usng generalzed bell membershp functon the membershp functon was dentfed. Fgure 3-8, depcts the ANFIS smulatons. DOI: / Page

5 Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. Fgure 3: ANFIS tranng Edtor Fgure 3, shows the ANFIS edtor nvoked wth anfsedt command. It shows clearly the components of smulaton. Fgure 4: Tranng Data Edtor Fgure 4, shows twenty expermental data portrayed across the edtor. The tranng cases are dspersed across the graph plots showng tranng cases wth vared patterns. Hybrd tranng was subsequently utlzed n tranng the obtaned data. At the far top rght, the nput s seen as nne (09) ntal varables, permutated as to producng 352 rules. Ths ntal nput produces a frng sngleton output. Fgure 5: ANFIS Testng Data Edtor DOI: / Page

6 Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. Fgure 5, shows the testng error matched across the output plot wth fve (05) tranng data plot wth an output of 0 to 0.4 wth a dataset ndex of 0 to 15. The dotted plot matches the testng data referenced aganst the tranng data. Fgure 6: ANFIS Structure Edtor Fgure 6, portray ANFIS structure encompasses all sx layers, ntated wth vared nput parameters and termnates wth an output. The nne ntegral nputs are captured on the far most left, succeed by membershp layer, wth rules layers succeedng the membershp layer and rules precede the weghted normalzaton layer. The defuzzfcaton ntated wth mddle of maxmum, beng a sugeno type nference system s captured wthn the ffth layer whle the sngleton output s seen as layer sx been the far most rght node (black). Fgure 7: ANFIS Tranng Error Edtor Fgure 7, portray the ANFIS tranng accommodated both across the horzontal and vertcal bar of the edtor whch dentfy the tranng error. The tranng was ntally set wth epoch of 30 and tolerance error of Overall tranng was attaned at epoch 1 wth DOI: / Page

7 PERCENTAGE SCORE Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. Fgure 8: ANFIS Tranng and Testng Edtor Fgure 8; portray the testng and tranng data matched across the output plot wth 20 tranng data accounted for. The algnment of each testng data wth tranng data hghlghts testng accuracy. The average accuracy testng error s V. Matrx Laboratory (MATLAB) Result Analyss The smulatons captured wth the aforementoned fgures produce these results; Number of nodes: 170 (100%), Number of lnear parameters: 64 (38%), Number of nonlnear parameters: 45 (26%), Total number of parameters: 109 (64%) and Number of tranng data pars: 15 (9%). Ths result was set wth a percentage range of 100% usng the formula X/y * 100(where x, stands for expresson value, y for the hghest column expresson value and 100, the benchmark percentage score. The graph of Fgure 9 exemplfy ths values 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% No. of Nodes BC-ANFIS GRAPH PLOT No. of Parameters No. of NonLnear Param. Total No. of Parameters Fgure 9: BC-ANFIS graph plot No. of trann Data Value Score Fgure 9, captures the graph plot for MATLAB BC-ANFIS smulaton. The graph plot wth parameters: no. of nodes, no of parameters, no. of nonlnear parameter, total no of parameter and no. of tranng data. The graph show clearly n parentage that number of nodes from the frst layer to the last layer had the DOI: / Page

8 Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer.. hghest percentage of 100%, followed by the total number of parameter equatng to over 60%, succeeded by no. of parameters wth over 35%, succeeded by no. of nonlnear parameters wth over 25% and number of tranng data havng the lowest score of about 9%. Therefore from the graph we can dentfy no of nodes as the most predomnant value score. VI. Concluson The smulaton of breast cancer expermental data usng Adaptve Neuro Fuzzy Inference System (BC- ANFIS) has been explored wth graphcal plot exemplfed n ths paper usng Matrx Laboratory (MATLAB). The MATLAB ANFIS edtors captured the tranng, testng, anfs structure and tranng error. Ths edtor captures the ntegral neuro-fuzzy smulatons whle the graphcal representaton dentfed the paramount mport of these parameters n terms of these smulatons. References [1]. CancerQuest, (2016), Cancer Detecton and Dagnoss, retreved onlne from dagnoss?gcld= [2]. MedcneNet, (2016), Breast Cancer, retreved onlne from [3]. Oncolex (2016), Breast Cancer symptoms, retreved from [4]. WrongDagnoss (2011), Breast cancer: Introducton/Causes and Symptoms, retreved from Ejofor C. I. Adaptve Neuro Fuzzy Inference System (ANFIS): MATLAB Smulaton of Breast Cancer Expermental Data. IOSR Journal of Computer Engneerng (IOSR-JCE), vol. 19, no. 4, 2017, pp DOI: / Page