January 2017 by Editor IJITCE

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING

JANUARY 2017 VOL-7 NO-01

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UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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International Journal of Innovative Technology & Creative Engineering Vol.7 No.01 January 2017

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From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. Virtual reality finally hitting the consumer market this year. Virtual reality headsets are bound to make their way onto a lot of holiday shopping lists. But new research suggests these gifts could also give some of their recipients motion sickness especially if theyâ&#x20AC;&#x2122;re women. So-called Virtual reality sickness also known as simulator sickness or cyber sickness. A high rate of people that you put in that are going to experience some level of symptoms. The company declined to comment on the new research but says it has made progress in making the virtual reality experience comfortable for most people, and that developers are getting better at creating Virtual reality content. All approved games and apps get a comfort rating based on things like the type of movements involved and Oculus recommends starting slow and taking breaks. This gender difference shows up in almost any situation that can cause motion sickness, like a moving car or a rocking boat. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.01 JANUARY 2017, IMPACT FACTOR: 1.04 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India

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Contents Medical Data Classification Using SVM and Neural Network Classifier-A Study P.Balamurugan, S.Vanitha .…………………………………….[400]

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Medical Data Classification Using SVM and Neural Network Classifier-A Study P.Balamurugan Assistant Professor, Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, India. Email:spbalamurugan@rediffmail.com S.Vanitha M.Phil Research Scholar, Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, India Abstract- This paper aimed at study and analyzing the machine learning algorithms and finding out most appropriate algorithm for medical data classification. In this study, designed a classification system using Neural Network and Support Vector Machine for medical data classification with various numbers of attributes and instances. It includes two kinds of classification experiments namely diseased and nondiseased data distribution from the Cleveland heart disease data set and class distribution of benign and malignant from the Breast Cancer Wisconsin (Original) Data Set. The experimental outcomes positively demonstrate that the Neural Network classifier is effective in undertaking medical data classification tasks which are concluded in the final chapter.

the 250 records of all attributes shown in the table I that have randomly selected from the database. Class distribution for the proposed work is benign (value 0) and malignant (value 1). TABLE I ATTRIBUTES OF B REAST C ANCER W ISCONSIN (O RIGINAL ) DATA S ET S.No.

Keywordsâ&#x20AC;&#x201D; Data Mining, Frequency Item Set, Apriori.

1. INTRODUCTION Medical data classification is a challenging task in the field of medical research. The medical record is very important for a patient as well as the doctor. Generally the medical record will help the doctor to classify the diseases, diagnose and give an appropriate treatment to the patient. In recent days, the volume of medical data is huge in size. So, it is very difficult to classify and understand the severity of the diseases manually. This paper is organised as follows. Section II describes the medical Breast Cancer and heart disease datasets. Section III covers the Results and discussion of the diseased and normal medical data classification. Section IV deals with the conclusion. 2. MEDICAL DATASETS This section explain about the medical datasets used for different experimental setup. The medical datasets include Breast Cancer Data and Cleveland Heart Data. A. Breast Cancer Wisconsin (Original) Data Set Breast Cancer Wisconsin (Original) Data Set[8] was created by WIlliam H. Wolberg (physician), University of Wisconsin Hospitals, Madison, Wisconsin, USA and donated by Olvi Mangasarian. It consists 10 attributes, 699 instances collected in the different occasions that are distributed into 8 groups. The prediction field refers to the presence of either benign or malignant. The proposed work experiments with

Attributes

Values

Sample code number

Id Number

Clump Thickness

1 - 10

Uniformity of Cell Size

1 - 10

Uniformity of Cell Shape

1 - 10

Marginal Adhesion

1 - 10

Single Epithelial Cell Size

1 - 10

Bare Nuclei

1 - 10

Bland Chromatin

1 - 10

Normal Nucleoli

1 - 10

Mitoses

1 - 10

B. Cleveland Heart Disease Data Set Cleveland heart disease data set[2] is a Multi- variate data set. It contains 76 attributes and 303 instances that are varying Categorical, Integer and Real. However, the proposed experiments refer to using a subset of 13 of them as shown in the table II. The prediction field refers to the presence of heart disease in the patient. The prediction field concentrated on simply attempting to distinguish presence of diseased (value 1) data from non-diseased (value 0).

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previous section. SVM uses an optimum linear separating hyper plane to separate two set of data in a feature space[5], [7]. The success rate of 91.9% is obtained for 10 attributes/features is shown in Table IV. Table III shows the confusion matrix for classifying the Breast cancer data set with fixed number of neurons. TABLE III CONFUSION MATRIX FOR CLASSIFYING THE BENIGN AND MALIGNANT DATA OF BREAST CANCER DATA SET WITH FIXED NUMBER OF NEURONS (20 NEURONS ) Predicted Class

ATTRIBUTES OF C LEVELAND H EART D ISEASE DATA S ET S.No.

Description

Attributes

age

Age in years

sex

Sex (1 = male; 0 = female)

Chest pain type

trestbps

chol

(1 = typical angina; 2 = atypical angina; 3 = non-anginal pain; 4 = Resting blood pressure(in mm Hg on admission to the hospital) asymptomatic) Serum cholestoral in mg/dl

fbs

Fasting blood sugar (> 120 mg/dl) (1 = true; 0 = false)

restecg

Resting electrocardiographic results

thalach

(0 = normal; 1 = having ST-T wave abnormality ) (T wave inversions and/or ST elevation or depression of > 0.05 mV)) Maximum heart rate achieved

exang

Exercise induced angina (1 = yes; 0 = no)

oldpeak

slope

ST depression induced by exercise relative to rest Slope of the peak exercise ST segment

Classifier

thal

Non-Diseased

Non-Diseased Diseased

Diseased

(1 = upsloping; 2 = flat; 3 = downsloping) 12

Actual Class

SVM

Number of major vessels (0-3) colored by flourosopy (3 = normal; 6 = fixed defect; 7 = reversable defect)

3. CLASSIFICATION RESULT Experiment-1: Understanding the classification performance of Benign and Malignant Data of Breast Cancer Wisconsin Data Set using NN Classi- fier: The experimental setup consists of 250 records consisting of both Benign and Malignant Data. Training and Testing sets are formed with random selection of 188 records and 62 records respectively. The 10 attributes/features as shown Table I are given as input to the classification system. These features are used as training parameters for the neural network using the scaled conjugate gradient algorithm with 20 hidden neurons for 1000 epochs[1], [3], [5]. Classification results of Benign and Malignant medical data is shown in Table IV & VII. The highest classification rate 95.2% is obtained for 10 features trained with 20 hidden neurons. The confusion matrix obtained for training neural network using 10 feature with 20 hidden neurons are shown in Tab. III. Figure 1 shows the Performance function plot of Neural Network classifier for the classification of Breast Cancer Data Set respectively. Experiment-2: Understanding the classification performance of Benign and Malignant Data of Breast Cancer Wisconsin Data Set using SVM Classifier: The Breast Cancer Data set used for SVM based classification system is similar to NN classification system defined in the

Non-Diseased 36

Diseased 2

30 1

2 30

Experiment-3: Understanding the classification performance of Heart Diseased and Non-Heart Diseased Data using NN Classifier: The experimental setup consists of 303 records consisting of both Heart Diseased and Non-Heart Diseased Data. Training and Testing sets are formed with random selection of 228 records and 75 records respectively. Each record has 13 attributes/features as shown Table II, are given as input to the classification system. These features are used as training parameters for the neural network using the scaled conjugate gradient algorithm with 20 hidden neurons for 1000 epochs[1], [3], [5]. Classification results of Heart Diseased and Non-Heart Diseased medical data is shown in Tab. VI & VII. The highest classification rate 85.5% is obtained for 13 features trained with 20 hidden neurons. The confusion matrix obtained for training neural network using 13 feature with 20 hidden neurons are shown in Tab. V. Figure 2 shows the Performance function plot of Neural Network classifier for the classification of Cleveland Heart Disease Data Set respectively. TABLE IV CLASSIFICATION RATE (%) OF BENIGN AND MALIGNANT FOR BREAST CANCER DATA (ACCACCURACY; TPR-TRU E POSITIVE RAT E ; TNR-TRU E NEGATIVE RAT E ) Data Sets ACC Recall TNR Precision Negative (TPR) Predictive SVM

91.9355

0.9231 0.9130 0.9474

0.8750

95.2381

0.9677 0.9375 0.9375

0.9677

Experiment-4: Understanding the classification performance of Heart Diseased and Non-Heart Diseased Data using SVM Classifier: The experimental setup used for Classification system using SVM is similar to NN

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classification system formed in the previous section. Here support vector machine classifier trained using the training data set taken from two groups(Heart Diseased and NonHeart Diseased) given by prediction column. SVM structure contains information about the trained classifier, including the support vectors, that is used by SVM Classifier for classification. Prediction is a column vector of values of the same length as Training set that defines two groups. Each element of Prediction column specifies the group the corresponding row of training belongs to. Prediction column can be a numeric vector, a string array, or a cell array of strings. SVM Training treats NaNs(Not a Numeric) or empty strings in Prediction as missing values and ignores the corresponding rows of training. SVM uses an optimum linear separating hyper plane to separate two set of data in a feature space. The success rate of 78.7% is obtained for TABLE V CONFUSION MATRIX FOR CLASSIFYING THE NON -DISEASED AND DISEASED DATA OF CLEVELAND HEART DISEASE DATA WITH FIXED NUMBER OF NEURONS (20 NEURONS ) Classifier

Actual Class

Non-Diseased

SVM

Non-Diseased

Diseased

Non-Diseased

Diseased

13 attributes/features is shown in Table VI. Table V shows the confusion matrix for classifying the Heart Diseased and Non-Heart Diseased Data set of Cleveland Heart Disease Data with fixed number of neurons. TABLE VI CLASSIFICATION RATE (%) OF NON -DISEASED AND DISEASED FOR CLEVELAND HEART DISEASE DATA (ACC-ACCURACY; TPR-TRUE POSITIVE RATE; TNR-TRUENEGATIVE RATE) Data Sets ACC Recall TNR Precision Negative (TPR) SVM NN

78.6667 0.7907 0.7813 0.8293 85.5263

0.7778 0.9250 0.9032

Predictive

results, Neural Network classifier gives prominent results in the classification of diseased and non- diseased data from each medical dataset. The major challenge of any classification problem is finding the effective/meaningful features from the number of attributes present in the data set. This work opens up the new interesting applications in medical data classification. Future contributions will concentrate on developing a novel and hybrid intelligent system for medical data classification.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

0.7353 0.8222

[8]

4. CONCLUSION Now-a-days, Health care system generates vast amount of information and it is accumulated in medical databases. So, the manual classification of this information is becoming more and more difficult. Therefore, there is an increasing interest in developing automated evaluation methods to follow up the diseases. In this paper, discussed the two class(diseased and non-diseased) classify problem. The proposed work used Neural Network and Support Vector Machine for classifying benchmark medical data sets namely Cleveland Heart Disease Dataset and Breast Cancer Wiscon- sin (Original) Dataset. Based on the experimental 402

REFERNCES Ben Krose, Patrick van der Smagt, “Introduction to Neural Networks,” The University of Amsterdam, 8th edition, 1996. Cleveland Clinic Foundation Heart disease data set available:http://archive.ics.uci.edu/ml/datasets/Heart +Disease. Faissal MILI, Manel HAMDI, “A hybrid Evolutionary Func- tional Link Artificial Neural Network for Data mining and Classification,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 8, 2012. R.C. Gonzalez, R.E. Woods, “Digital Image Processing,” Sec- ond Ed. Prentice-Hall, New Jersey, 2002. Jianxin Chen, Yanwei Xing, Guangcheng Xi, Jing Chen, Jianqiang Yi, Dongbin Zhao, Jie Wang, “A Comparison of Four Data Mining Models: Bayes, Neural Network, SVM and Decision Trees in Identifying Syndromes in Coronary Heart Disease,” Advances in Neural Networks - ISNN 2007, Lecture Notes in Computer Science, Vol. 4491, pp. 1274-1279, 2007. Mai Shouman, Tim Turner, Rob Stocker, “Applying k-nearest neighbor in diagnosing heart disease patients,” International Journal of Information and Education Technology, Vol. 2, No.3, pp. 220-223, June 2012. Sandhya Joshi, Hanumanthachar Joshi, “SVM Based Clinical Decision Support System for Accurate Diagnosis of Chronic Obstructive Pulmonary Disease,” International Journal of Engineering Research & Technology(IJERT), Vol. 2, No. 4, April 2013. UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets.html

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