ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING
MARCH 2017 VOL-7 NO-03
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.03 MARCH 2017, IMPACT FACTOR: 1.04
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: firstname.lastname@example.org 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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.03 MARCH 2017, IMPACT FACTOR: 1.04
International Journal of Innovative Technology & Creative Engineering Vol.7 No.03 March 2017
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.03 MARCH 2017, IMPACT FACTOR: 1.04
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. DNA from the 7,700-year-old remains of two women is surprisingly similar to that of people living in that area today, researchers report. Their finding suggests that at least some people in East Asia have not changed much over the last 8,000 years. In Western Europe especially, scientists studying ancient DNA have put together a picture of flux, says study coauthor Andrea Manica. Every few thousand years, there are major turnovers of people. In DNA collected from the bones and teeth of these ancient peoples, scientists can spot genetic signatures of different populations. 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
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.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.03 MARCH 2017, IMPACT FACTOR: 1.04 Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. 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 Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 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 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.03 MARCH 2017, IMPACT FACTOR: 1.04 Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.03 MARCH 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 Diabetic Disease Identifications Using Classification Technique in Orange Tool Dr.R.Shanmugasundaram & Dr.S.Prasath .…………………………………….
Diabetic Disease Identifications Using Classification Technique in Orange Tool Dr.R.Shanmugasundaram Associate Professor, Department of Computer Science, Erode Arts and Science College (Autonomous), Erode, Tamil Nadu, India. Email: email@example.com Dr.S.Prasath Assistant Professor, Department of Computer Science, Nandha Arts and Science College Erode, Tamil Nadu, India. Email: firstname.lastname@example.org Abstract-Data mining is a process of extracting information from a dataset and transforms it into understandable structure to discover patterns in large data sets. Data mining for healthcare is useful in evaluating the effectiveness of clinical treatments to its roots in databases records system getting to know and facts visualization. Diabetic ailment refers back to the heart disorder that develops in persons with diabetes. The term diabetes is a continual ailment that occurs both when the pancreas does now not produce sufficient insulin. The blood vessels despite the fact that many data mining type techniques exist for the prediction of heart disorder there is inadequate records for the prediction of heart illnesses in a diabetic character. A number of experiments had been conducted the use of orange tools for contrast of the performance of predictive facts mining techniques on the diabetic dataset with attributes. The SVM classifier method has been carried out in orange tool prediction model using minimal training set to diagnose vulnerability of diabetic sufferers. All the above experiments find the probabilities of risk in diabetic patients for coronary heart sickness. Keywordsâ€” Data Mining, Diabetic, Heart, Orange, SVM.
1. INTRODUCTION Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart. Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal model and test the ability of classification algorithms with state of the art parties in global health care domain. A number of experiments have been conducted using weka and orange tools for comparison of the performance of predictive data mining techniques on the diabetic dataset with 1000 records using different attributes. In this work naive Bayes data mining classifier
technique has been applied in weka and orange tools produces an optimal prediction to diagnose of diabetic patients. 2. RELATED WORKS Anuja Kumari et al.  described the Support vector machine, a supervised machine learning method as the classifier for diagnosis of diabetes using Pima Indian diabetic database in Classification of Diabetes Disease Using Support Vector Machine. Asha Gowda Karegowda et al.  describes diabetes can occur in anyone. However, people who have close relatives with the disease are somewhat more likely to develop it. Other risk factors include obesity, high cholesterol, high blood pressure and physical inactivity. The risk of developing diabetes also increases, as people grow older. People who are over 40 and overweight are more likely to develop diabetes, although the incidence of type-2 diabetes in adolescents is growing. Jayshri Sonawane et al.  presented the heart is the organ that pumps blood, with its life giving oxygen and nutrients, to all tissues of the body. If the pumping action of the heart becomes inefficient, vital organs like the brain and kidneys suffer and if the heart stops working altogether, death occurs within minutes. The term heart disease applies to a number of illnesses that affect the circulatory system, which consists of heart and blood vessels. Jianchao Han et al.  analyzed a Pima Indians diabetes data set containing information about patients with and without diabetes. This work focuses on data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction. Karthikeyani et al.  presented the classification of supervised data mining algorithms based on diabetes disease dataset in Comparative of Data mining classification algorithm in Diabetes disease Prediction. Sarojini Balakrishnan et al.  proposed a system to improve the diagnostic accuracy of diabetic disease by selecting informative features of Pima Indians Diabetes dataset in Empirical Study on the Performance of Integrated Hybrid Prediction Model on the Medical Datasets. They propose a hybrid prediction model that combines two different
functionalities of data mining clustering and classification with F-score selection approach to identify the optimal feature subset of the Pima Indians Diabetes dataset. Selvakuberan et al.  presented the diabetes is one of the major causes of premature illness and death worldwide. In developing countries, less than half of people with diabetes are diagnosed. There is no time for diagnoses and adequate treatment, complications and morbidity from diabetes rise exponentially. Vahid Rafe et al.  developed the medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. Medical diagnosis is regarded as an important yet complicated task that needs to be executed accurately and efficiently. Vijayarani et al.  discussed the heart disease plays an important role in data mining due to occurrence of death in heart diseases. To reduce cost for achieving clinical tests an appropriate computer based information and decision support should be provided. 3. METHODOLOGY A major problem of traditional strategy of encoding is the high dimensionality of the feature vector. The feature vector with a large number of key terms is not only unsuitable for neural networks but also easily to cause the over fitting problem. Each algorithm requires submission of data in a specified format. The conversion of raw data into machine understandable format is called preprocessing. The data preparation phase covers all activities to construct the final dataset from the initial raw data. These raw data can be stored in several formats including text, excel or other database types of files. Then the raw data is changed into data sets with a few appropriate characteristics.
Algorithm Step 1: Load the dataset and divides the data into training set. Step 2: Generate random weights for each input data point. Step 3: Determine the value of the bias term b and initialize the error for each point randomly. Step 4: Initialize random values. Step 5: Apply SVM classifier algorithm to train the data are identified. Step 6: Calculate number of support vectors. Step 7: Loop until stopping criteria is met, usually until reach maximum number of iterations. Step 8: Identify the class label for new test data. Step9: Performance is evaluated for SVM classifier using predicted class label for test data and expected class labels using confusion matrix.
4. EXPERIMENTATION AND RESULTS The predictive facts mining techniques on the diabetic dataset with attributes are collected from the Pima diabetic database. The experimentation is carried out by Orange. Some of the sample data are experimented and is presented in the Fig.4.1 to Fig. 4.3. 4.1 ORANGE Orange is a component-based data mining and machine learning software suite, featuring a visual programming front- end for explorative data analysis and visualization and Python bindings and libraries for scripting. It includes a set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation and exploration techniques. Its graphical user interface builds upon the cross-platform framework
3.1 NAIVE BAYES APPROACH Naive Bayes classifier as a term dealing with a simple probabilistic classifier based on application of Bayes theorem with strong independence assumptions. Since independent variables are assumed, only the variances of the variables for each class need to be determined. It can be used for both binary and multi class classification problems. Naive Bayes data mining classifier technique has been applied which produces an optimal prediction model using minimum training set to predict the chances of diabetic patient getting heart disease. The diagnosis of diseases plays vital role in medical field.
Fig 4.1 Explorer in ORANGE
6. CONCLUSION Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and ensures detection of fraud and abuse. The data mining techniques give the necessary standard in prediction. The performance in prediction depends on the various attributes which are helpful in predicting disease efficiently and patients receive better and more affordable healthcare services. The SVM data mining classifier technique has been applied which produces an optimal prediction model using minimum training set to predict the chances of diabetic patient. Orange tool is considered being a successful tool for classification purpose and evidence is the proposed system is quite good, since it has proved and shown good accuracy on the prediction of diabetic. To determine the most accurate technique to predict the risk in diabetic patients. The diabetic patients based on their predictive accuracy. In overall accuracy, in terms of precision and recall exhibited a very consistent performance.
Fig 4.2 Naive Bayes Diabetic Dataset Classifier Predicted Output
Fig 4.4 SVM Diabetic Dataset Classifier Predicted Output
5. PERFORMANCE EVALUATION To measure the performance sensitivity, accuracy and specificity are used. TP is true positive, FP is false positive, TN is true negative and FN is false negative. TPR is true positive rate, which is equivalent to Recall. True Positive Rate Sensitivity= ...... equ.1 ( True Positive + False Negative) Specificity=
True Negative Rate ...... equ.2 ( False Positive + True Negative) Accuracy=
TP+TN ...... equ.3 ( TP+ TN+FP+FN)
Table.5.1 Comparison Results
Methods / Parameters
Number of Instances Accuracy
Naive Bayes Classifier 788 82.74%
SVM Classifier 788 85.86%
From the above table 5.1 shows the performance of SVM classifier. The fig.5.1 shows comparison graphical representation of methods. The method can over perform the traditional method with classify recall rate.
REFERENCES  Anuja Kumari, V and Chitra, R 2013, ‘Classification of Diabetes Disease Using Support Vector Machine’, International Journal of Engineering Research and Applications, vol. 3, no. 2, pp. 1797-1801.  Asha Gowda Karegowda, Manjunath AS and Jayaram MA 2011, ‘Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis of Pima Indians Diabetes’, International Journal on Soft Computing, vol. 2, no. 2, pp.15-23.  Jayshri Sonawane, S, Dharmaraj Patil, R & Vishal Thakare, S 2013, ‘Survey on Decision Support System For Heart Disease, International Journal of Advancements in Technology, vol.4, no.1, pp. 89-96.  Jianchao Han, Juan Rodriguze & Mohsen Beheshti 2008, ‘Diabetes Data Analysis and Prediction Model Discovery Using Rapid Miner’, In Proceedings of the 2nd International Conference on Future Generation Communication and Networking, vol.3, pp. 96-99.  Karthikeyani, V & Parvin Begum 2012, ‘Comparative of Data mining classification algorithm in Diabetes disease Prediction’, International Journal of Computer Applications, vol. 60, no. 12, pp. 26-31.  Sarojini Balakrishnan, Ramaraj Narayanaswamy & Ilango Paramasivam 2011,‘An Empirical Study on the Performance of Integrated Hybrid Prediction Model on the Medical Datasets’, International Journal of Computer Applications, vol.29, no.5, pp. 1-6.  Selvakuberan, K, Kayathiri, D, Harini, B & Indra Devi, M 2011, ’An Efficient Feature Selection Method for Classification in Health care Systems using Machine Learning Techniques’, In Proceedings of the 3rd International Conference on Electronics Computer Technology, Kanyakumari, India vol. 4, pp. 223-226.  Vahid Rafe & Roghayeh Hashemi Farhoud 2013, ‘A Survey on Data Mining Approaches in Medicine’, International Research Journal of Applied and Basic Sciences, vol.4, no.1, pp. 196-202.  Vijayarani, S & Sudha, S 2013, ‘An Effective Classification Rule Technique for Heart Disease Prediction’,International Journal of Engineering Associates, vol. 1, no.4, pp. 81-85.
Fig 5.1 Comparison Values
International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) vol7no03