December2017

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

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING

DECEMBER 2017 VOL- 7 NO-12

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 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: 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 AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 2017, IMPACT FACTOR: 1.04

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International Journal of Innovative Technology & Creative Engineering Vol.7 No.12 December 2017

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 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.

The satellite is fueling scientists’ dreams of a future safe from hacking of sensitive communiqués. One day, impenetrable quantum cryptography could protect correspondences. A secret string of numbers known as a quantum key could encrypt a credit card number sent over the internet, or encode the data transmitted in a video call, for example. That quantum key would be derived by measuring the properties of quantum particles beamed down from such a satellite. Quantum math proves that any snoops trying to intercept the key would give themselves away.

“Quantum cryptography is a fundamentally new way to give us unconditional security ensured by the laws of quantum physics,” says Chao-Yang Lu, a physicist at the University of Science and Technology of China in Hefei, and a member of the team that developed the satellite. But until this year, there’s been a sticking point in the technology’s development: Long-distance communication is extremely challenging, Lu says. That’s because quantum particles are delicate beings, easily jostled out of their fragile quantum states. In a typical quantum cryptography scheme, particles of light called photons are sent through the air, where the particles may be absorbed or their properties muddled. The longer the journey, the fewer photons make it through intact, eventually preventing accurate transmissions of quantum keys. So quantum cryptography was possible only across short distances, between nearby cities but not far-flung ones.

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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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 2017, IMPACT FACTOR: 1.04

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.12 DECEMBER 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

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 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

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 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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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 2017, IMPACT FACTOR: 1.04 Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.

MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran.

Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India.

Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

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Contents Performance Comparison of Data Mining Techniques

T.Praveena & Dr. S.Prasath

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 2017, IMPACT FACTOR: 1.04

Performance Comparison of Data Mining Techniques T.Praveena Ph.D Research Scholar, Department of Computer Science, Nandha Arts and Science College, Erode, Tamil Nadu, India.

Email: praveenasurya14@gmail.com Dr.S.Prasath Research Supervisor & Assistant Professor, Department of Computer Science, Nandha Arts and Science College, Erode, Tamil Nadu, India.

Email:softprasaths@gmail.com AbstractIn recent years the healthcare industry has generated large amounts of data. The value based treatment in hospitals and digitization of world likes to have the computerized data rather than hard copy form.

Diabetes is one of the common and rapidly increasing diseases in the world. It is a major health problem in most of the countries. Diabetes is a condition in which your body is unable to produce the required amount of insulin needed to regulate the amount of sugar in the body. This leads to various diseases including heart disease, kidney disease, blindness, nerve damage and blood vessels damage. Hence, there is a requirement of a model that can be developed easily providing reliable, faster and cost effective methods to provide information of the probability of a patient to have diabetes. In this paper dealt with an attempt is made a comparative study on diabetes using classification and clustering.

Keywords- Data Mining, Big Data, Health Care, Diabetes, Clustering, Classification.

1. INTRODUCTION Digital data have a vital role in the computerized world. Now we are living in data world. Everywhere we are seeing only data. The storage of data occupies more space, since the data usage is increasing every day. The important thing is ‘data storing’ and ‘data processing’. The data in computing are represented as structured format in the olden days. It is stored as tabular format. Now a days so much data is not natively in structured format. The problems start right away during data acquisition, when the massive data requires us to make decisions, about what data to retain and what to remove and how to store what we save reliably with the correct metadata.

Dealing with these Big Data is a highly challenging issue for the data analysts.

Data Mining Data mining is an extraction of hidden predictive information from large database. Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets.

These tools can include statistical models, mathematical algorithms, and machine learning methods.

Consequently, data mining consists of more than collecting and managing data; it also includes analysis and prediction [1]. With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and perhaps interpretation of such data and for the extraction of interesting knowledge that could help in decision-making.

Limitation of Data Mining: i.

Data Mining are primarily data or personnel-related rather than technology-related. ii.

Data Mining difficult to handle the large amount of data. iii.

It is difficult to handle the dynamic data. iv.

Big data can handle enormous amount data and also it capable of handle the dynamic data .So that Big data is better than data mining to handle such kind of data and easily solve the problems.

Big Data Big data is a buzzword that is used to describe the large amount of data either structured or unstructured data format. Exactly, if the data which is beyond to the storage capacity & which is beyond to the processing power, that data we are calling ‘BIG DATA’. Big data is so large and is difficult to process using the old database and software techniques. The health care data includes Electronic Health Reports (EHR) of patients data, clinical reports, doctor’s prescription, diagnostic reports, medical images, pharmacy information, health insurance related data, data from Social Medias and medicinal journals [13]. All these information collectively forms Big Data in health care. By employing the analysis of big data will produce the predicted results for understanding the trends to improve the health care and life time expectancy, proper treatment at early stages at low cost.

The analytics associated with big data is described by four characteristics: volume, velocity, variety and veracity [8].

The accumulation of health-related data continuously, resulting in an incredible volume of data; Velocity is accessing those data in real-time at a rapid speed; Variety includes diabetic glucose measurements, blood pressure readings, and various EHRs; Whereas veracity assumes the simultaneous scaling up in performance of the architectures and platforms, algorithms and tools to match the need of big data Having data larger it needs different Approaches, Techniques, Architectures, and Tools.

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Healthcare Big data and its related technologies have improved healthcare enormously from understanding the Origins of diseases, Better diagnoses, Helping patients to monitor their own conditions. Healthcare organizations can improve their quality of service by analyzing the effectiveness of a treatment and also the efficiency of the healthcare delivery process. Since information is in the digital form, healthcare providers can use some available tools and technologies to analyze that information and generate valuable insights. Full view for every patient is created by electronic health records, scanned documents, medical images, notes from physicians, information about environment.

Diabetes Diabetes is a disease that arises when the insulin production in the body is insufficient or the body is unable to use the produced insulin in an appropriate manner, as an outcome, this leads to high blood glucose. The body cells break down the food into glucose and this glucose needs to be transported to all the cells of the body. The insulin is the hormone that directs the glucose that is produced by breaking down the food into the body cells. Any modification in the creation of insulin leads to a rise in the blood sugar levels and this can lead to harm to the tissues and failure of the organs.

Generally a person is considered to be suffering from diabetes, when blood sugar levels are above normal (4.4 to 6.1 mmol/L).

There are three main types of diabetes, viz. Type 1, Type 2 and Gestational.

Types of Diabetes The three main types of diabetes are described below: 1. Type 1 – There are only around 10% of diabetes patients have this form of diabetes. But, there has been a rise in the number of cases of this type in the world. The disease manifest as an autoimmune disease happening at a very young age of below twenty years. It called as juvenile-onset diabetes. In this type of diabetes, the pancreatic cells that produce insulin have been demolished by the defence system of the body. Injections of insulin along with regular blood tests and dietary limits have to be followed by patients suffering from Type 1 diabetes.

2. Type 2 – Almost 90% of the diabetes patients are affected by this type 2 diabetes. It called as, the adult-onset diabetes or the non-insulin dependent diabetes. In this situation the various organs of the body become insulin resistant, and this raises the demand for insulin. At this point, pancreas doesn’t make the required amount of insulin [4].To keep this type of diabetes away, the patients have to follow a strict diet, routine exercises and keep track of the blood glucose. Obesity, overweight, physically inactive can lead to type 2 diabetes. Also with ageing, the risk of emerging diabetes is measured to be more.

Most of the Type 2 diabetes patients are in border line diabetes or the Pre-Diabetes, a situation where the blood glucose levels are upper than normal but not as high as a diabetic patient.

3. Gestational diabetes – It tends to occur in pregnant women due to the high sugar levels as the pancreases don’t produce enough amount of insulin. Taking no treatment can lead to difficulties during child birth [4]. Monitoring the diet and taking

insulin can control this form of diabetes. All these types of diabetes are serious and It needs treatment.

Symptoms, Diagnosis and Treatment The common symptoms of a person suffering from diabetes are: a. Polyuria (frequent urination) b. Polyphagia (excessive hunger) c. Polydipsia (excessive thirst) d. Weight gain or strange weight loss. e. Healing of wounds is not quick, blurred vision, fatigue, itchy skin, etc Urine test and blood tests are conducted to detect diabetes by checking for excess body glucose. The commonly conducted tests for determining whether a person has diabetes or not are i.

A1C Test ii.

Fasting Plasma Glucose (FPG) Test iii.

Oral Glucose Tolerance Test (OGTT).

Though both Type 1 and Type 2 diabetes cannot be cured they can be controlled and treated by special diets, regular exercise and insulin injections. The complications of the disease include neuropathy, foot amputations, glaucoma, cataracts, increased risk of kidney diseases and heart attack and stroke and many more.

2. RELATED WORKS This section is to provide the general overview of related works in the field of diabetes. In particular those works are related and focused on the clustering and classification techniques and its various methods.

To conduct a systematic review ofthe applications of machine learning, data mining techniques and its tools in the field of diabetes [5].The predictive analysis algorithm in Hadoop/Map Reduce environment to predict the diabetes type’s prevalent, complications associated with it and the type of treatment to be provided [12].Diabetes mellitus and its spread over the country particularly in Tamil Nadu, theresearch region. It already developing medical intelligence using clinical big data and proposes aforecasting and prediction system for Diabetes mellitus[10].To find the solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing. To propose a quicker and more efficienttechnique of diagnosing the disease, leading to timely treatment of the patients[1].

A hybrid algorithm of Modified-Particle Swarm Optimization and Least Squares Error.Support Vector Machine is proposed for the classification of type II DM patients. LS-SVM algorithm is used for classification by finding optimal hyper-plane which separates various classes.Since LS-SVM is so sensitive to the changes of its parameter values, Modified-PSO algorithm is used as an optimization technique for LS-SVM parameters [8].The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or

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other general applications [11].The modified J48 classifier is used to increase the accuracy rate of the data mining procedure for the diabetes [4].

To classify the risk of diabetes mellitus. Four well known classification models that are Decision Tree, Artificial Neural Networks, Logistic Regression and Naive Bayes were first examined.Then, findings suggest that the best performance of disease risk classification is Random Forest algorithm is best for the diabetes [7].The adaboost and bagging ensemble techniques using J48 (c4.5) decision tree as a base learner along with standalone data mining technique J48 to classify patients with diabetes mellitus using diabetes risk factors. The performance of adaboost ensemble method is better than bagging as well as standalone J48 decision tree [11].

A survey also highlights applications, challenges and future issues of Data Mining in healthcare.

Recommendation regarding the suitable choice of available Data Mining technique [3].The Classification of diabetic’s data set and the K-means algorithm to categorical domains.

Before classify the data set preprocessing of data set is done to remove the noise in the data set. This algorithm is also used to improve the classification rate and cluster the data set using two attributes namely plasma and pregnancy attribute [6].

Three data mining algorithms, namely SelfOrganizing Map (SOM), C4.5 and RandomForest, are applied on adult population data from Ministry of National Guard Health Affairs (MNGHA), Saudi Arabia to predict diabetic patients using 18 risk factors. RandomForest achieved the best performance compared to other data mining classifiers [13].To concentrate upon predictive analysis of diabetes diagnose using artificial neural network as a data mining technique. The Pima Indian diabetes database was obtained from UCI server and used for analysis [2].

In the literature review mainly focused on the analysis of diabetes. Clustering and classification techniques are used to identify the diabetes. This system provides an efficient way to cure and care the patients with better outcomes like affordability and availability.

3. Clustering This pattern partitions the records in database into diverse gatherings. In the same gathering, the gatherings have the comparative properties and the distinctions ought to make as bigger as could be expected under the circumstances and in the same gathering, the distinctions ought to be as littler as would be prudent. There is no predefined class in this gathering it goes under the unsupervised learning. Techniques included in bunch examination are partioning systems, various leveled routines, thickness Based strategies, network based techniques, model-based routines, grouping highdimensional information, requirement based bunching and Outlier investigation. i. K-means Clustering ii. Hierarchical clustering iii.Density based clustering

4. Classification Classification divides data samples into target classes.

The classification technique predicts the target class for each data points. For example, patient can be classified as “high risk” or “low risk” patient on the basis of their disease pattern using data classification approach. It is a supervised learning approach having known class categories. Binary and multilevel are the two methods of classification. In binary classification, only two possible classes such as, “high” or “low” risk patient may be considered while the multiclass approach has more than two targets for example, “high”, “medium” and “low” risk patient.Figure 1 shows the various classification techniques used to identify the diabetes in easier way.

Fig. 3.1 Different Classification Techniques The research work revealed that there is no single best algorithm which yields better result for dataset. Classification techniques are also used for predicting the treatment cost of healthcare services which is increases with rapid growth every year and is becoming a main concern for everyone [7].

Classification tree approach to predict the cost of healthcare [8] by using the dataset of 3 years collected from the insurance companies to perform the experiment. The first two year data was used to train the classifier and last one year data was used for comparing the predicted results of classifier. Finally there are various classification technique are used to identify the diabetes prediction.

5. Data Mining Tools The data mining tools on which the integrated clustering-classification technique has been implemented.

5.1 WEKA tool WEKA is Waikato Environment for Knowledge Analysis, data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand. It is a collection of open source of many data mining and machine learning algorithms, including pre-processing on data, classification, regression, clustering, association rule extraction and feature selection which supports .arff (attribute relation file format) file format.

5.2 Tanagra Tanagra was written an aid to education and research on data mining by Ricco Rakotomalala. The entire user operation of Tanagra is based on the stream diagram paradigm.

Under the stream diagram paradigm, a user builds a graph specifying the data sources and operations on the data. Paths through the graph can describe the flow of data through manipulations and analysis. Tanagra simplifies this paradigm by restricting the graph to be a tree with only one parent to each node and the other one for data source of an each operation.

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5.3 Orange Orange is a component-based data mining and machine learning software suite, featuring a visual programming front-end for explorative data analysis, visualization, Python bindings and libraries for scripting. It includes set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation and exploration techniques. It is implemented in C++ and Python.

6. Experiments and Results The dataset "pima Indian Diabetes" are consider with the use of K-means, Hierarchical and Density based clustering technique and the different classification algorithms available on data mining tools. The Pima Indian diabetes data sets available on UCI machine learning repository.

The experiment is performed on the dataset results in Table 6.1 shows the accuracy measure of K-means clustering technique for different classifiers used. SVM provides the highest accuracy in the range of 66-71%, followed by Naïve Bayes with accuracy in the range of 64-66% and KNN with accuracy ranging between 62-68%.

Table 6.1 Accuracy for K-means clustering Classifier

Weka

Tanagra

66.18 %

64.87%

65.38%

KNN

68.71 %

62.11%

62.90%

[2]

SVM 71.13 % 66.45% 66.17% The experiment is performed on the dataset results in Table 6.2 shows the accuracy measure of K-means clustering technique for different classifiers used. SVM provides the highest accuracy in the range of 64-68%, followed by Naïve Bayes with accuracy in the range of 61-67% and KNN with accuracy ranging between 60-66%.

Table 6.2 Accuracy for Hierarchical clustering Weka

Tanagra

Orange

NB

61.20 %

62.14%

67.14%

KNN

60.84 %

65.74%

66.28%

SVM

64.45 %

66.12%

68.17%

Weka

Tanagra

Orange

NB

60.12 %

61.14%

62.78%

KNN

61.27 %

62.45%

63.54%

SVM

68.23 %

63.18%

64.27%

[3]

[4]

[5]

The experiment is performed on the dataset results in Table 6.3 shows the accuracy measure of K-means clustering technique for different classifiers used. SVM provides the highest accuracy in the range of 63-68%, followed by Naïve Bayes with accuracy in the range of 60-62% and KNN with accuracy ranging between 60-62%.

Table 6.3 Accuracy for Density based clustering Classifier

[1]

Orange

NB

Classifier

6. CONCLUSION This paper dealt with the survey of automatic diagnosis of diabetes is an important real-world medical problem. Detection of diabetes in its early stages is the key for treatment. Clustering, classification techniques and various methods are used to model actual diagnosis of diabetes for local and systematic treatment, along with presenting related work in the field. The performance of the techniques was investigated for the diabetes diagnosis problem. This research work also shows the importance of the diabetes approach for the performance of classification and clustering techniques it shows better result for the patients. In future it is planned to gather the information from different locales over the world and make a more precise and general prescient model for diabetes conclusion. Future study will likewise focus on gathering information from a later time period and discover new potential prognostic elements to be incorporated. The work can be extended and improved for the automation of diabetes analysis.

[6]

[7]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.12 DECEMBER 2017, IMPACT FACTOR: 1.04

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Saravana kumar N, Eswari T, Sampath & Lavanya , “Predictive Methodology for Diabetic Data Analysis in Big Data”, 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15).

Tahani Daghistani, Riyad Alshammari “Diagnosis of Diabetes by Applying Data Mining Classification Techniques”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 7, 2016

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