ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING
AUGUST 2016 VOL -6 NO - 8
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL. 6 NO.8 AUGUST2016 IMPACT FACTOR: 0.61
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. 6 NO.8 AUGUST2016 IMPACT FACTOR: 0.61
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International Journal of Innovative Technology & Creative Engineering Vol.6 No.8 August 2016
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL. 6 NO.8 AUGUST2016 IMPACT FACTOR: 0.61
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. In the space business, weight and size are what run up the bills. So imagine the appeal of a telescope that’s a tenth to as little as a hundredth as heavy, bulky and power hungry as the conventional instruments that NASA and other government agencies now send into space. Especially alluring is the notion of marrying the time-tested technology called interferometry, used in traditional observatories, with the new industrial field of photonics and its almost unimaginably tiny optical circuits. Say hello to SPIDER or Segmented Planar Imaging Detector for Electro-optical Reconnaissance. Some doubt it will ever work. Researchers at the Lockheed Martin Advanced Technology Center in Palo Alto, Calif, with partners in a photonics lab at the University of California, Davis have described work on SPIDER for several years at specialty conferences. Exposure to air pollution at the place of residence increases the risk of developing insulin resistance as a prediabetic state of type 2 diabetes. Scientists of Helmholtz Zentrum München, in collaboration with colleagues of the German Center for Diabetes Research (DZD), reported these results in the journal Diabetes. Whether the disease becomes manifest and when this occurs is not only due to lifestyle or genetic factors, but also due to traffic-related air pollution," said Professor Annette Peters, director of the Institute of Epidemiology II at Helmholtz Zentrum München and head of the research area of epidemiology of the DZD. For the current study, she and her colleagues in collaboration with German Diabetes Center Düsseldorf and the German Heart Centre analyzed the data of nearly 3,000 participants of the KORA study who live in the city of Augsburg and two adjacent rural counties. All individuals were interviewed and physically examined. Furthermore, the researchers took fasting blood samples, in which they determined various markers for insulin resistance and inflammation. In addition, leptin was examined as adipokine which has been suggested to be associated with insulin resistance. Non-diabetic individuals underwent an oral glucose tolerance test to detect whether their glucose metabolism was impaired. 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.
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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.
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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. 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
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Contents A Novel Technique for Prediction of Diabetic Patients Data Using Naives Bayes Classification in Orange Tool S.Nivetha & Dr.A.Geetha ……………….…………………………………….[367]
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.8 AUGUST 2016, IMPACT FACTOR:0.61
A Novel Technique for Prediction of Diabetic Patients Data Using Naives Bayes Classification in Orange Tool S.Nivetha Assistant Professor, Department of Computer Science, Kamban Arts and Science College, Pollachi, Tamil Nadu, India. Email: nive0501@gmail.com Dr.A.Geetha Assistant Professor, PG & Research Department of Computer Science, Chikkanna Govt. Arts College, Tirupur, Tamil Nadu, India. Email: gee_sam@yahoo.com Abstract - Data mining is a process of extracting information from a dataset and transform 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 naive bayes 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. In this test a comparative examine has been performed at the classifiers which result in the chance of diabetic patients getting heart disease from a system. The performances additionally had been in comparison the use of accuracy and additionally in terms of precision and exhibited a great overall performance. Keywords- Data Mining, Diabetic Data, RF, NB, ORANGE.
1. INTRODUCTION Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Flat files: Flat files are actually the most common data source for data mining algorithms, especially at the research level. Flat files are simple data files in text or binary format with a structure known by the data mining algorithm to be applied. Relational Databases: It consists of a set of tables containing either values of entity attributes or values of attributes from
entity relationships. Tables have columns and rows, where columns represent attributes and rows represent tuples.
Fig 1.1 Steps in Data mining Data Warehouses: A data warehouse as a store house is a
repository of data collected from multiple data sources and is intended to be used as a whole under the same unified schema. Transaction Databases: A transaction database is a set of records representing transactions, each with a time stamp, an identifier and a set of items. Multimedia Databases: Multimedia databases include video, images, audio and text media. They can be stored on extended object-relational. Multimedia is characterized by its high dimensionality, which makes data mining even more challenging. Spatial Databases: Spatial databases are databases that, in addition to usual data, store geographical information like maps and global or regional positioning. World Wide Web: The World Wide Web is the most heterogeneous and dynamic repository available. A very large number of authors and publishers are continuously contributing to its growth and metamorphosis, and a massive number of users are accessing its resources daily. Time-Series Databases: Time-series databases contain time related data such stock market data or logged activities. These databases usually have a continuous flow of new data coming in, which sometimes causes the need for a challenging real time analysis. 2. RELATED WORKS Anuja Kumari et al. [1] 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
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.8 AUGUST 2016, IMPACT FACTOR:0.61 Vector Machine. They have used the basic concepts of SVM and kernel function selection and experiments have been conducted on Matlab. Asha Gowda Karegowda et al. [2] 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. [3] 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. [4] analyzed a Pima Indians diabetes data set containing information about patients with and without diabetes. This work focuses on data pre-processing, 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. [5] presented the classification of supervised data mining algorithms based on diabetes disease dataset in Comparative of Data mining classification algorithm in Diabetes disease Prediction. Different classification algorithms like C4.5 decision tree, Classification and Regression Trees, Support Vector Machine, K-Nearest Neighbour and Prototype Neural Network classification have been used to analyze the Pima Indian Diabetes dataset with 9 attributes and 768 instances. Sarojini Balakrishnan et al. [6] 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. [7] 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. [8] 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. Data mining technology provides a user oriented approach to novel and hidden patterns in the data. Medical diagnosis is regarded as an important yet complicated task that needs to be executed accurately and efficiently.
Vijayarani et al. [9] discussed the heart disease plays an important role in data mining due to occurrence of death in heart diseases. Medical diagnosis plays a vital role and it is yet a complicated task that needs to be executed efficiently and accurately. To reduce cost for achieving clinical tests an appropriate computer based information and decision support should be provided. Vijaya Lakshmi et al. [10] discussed Gestational Diabetes Mellitus is defined as any abnormal carbohydrate that begins or is first recognized during pregnancy. It does not exclude the possibility that unidentified glucose intolerance have preceded the pregnant state. . 3. DATA MINING TOOL 3.1 ORANGE Orange is a component-based data mining and machine learning software suite, featuring a visual programming frontend 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 crossplatform framework
Fig 3.1 Explorer in ORANGE
Fig 3.2 Diabetic Attributes Selected in ORANGE
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.8 AUGUST 2016, IMPACT FACTOR:0.61 4.1 EXISTING METHODOLOGY 4.1.1 J48 Pruned Tree J48 is a module for generating a pruned or unpruned C4.5 decision tree. When we applied J48 onto refreshed data, got the results shown as below on Figure.
Fig 4.1 J48 Diabetic Dataset Classifier Output Predicted 4.1.2 Random Forest Random forest is an algorithm that consists of many decision trees. The model uses a bagging approach and the random selection of features to build a collection of decision trees with controlled variance. The instances class is to the class with the highest number of votes, the class that occurs the most within the leaf in which the instance is placed. By using trees that classify the instances with low error the error rate of the forest decreases. The correlation and strength of the forest increases with the number m of variables selected. A smaller m returns a smaller correlation and strength. To improve the prediction’s accuracy, a bootstrap method is used to create different trees.
labels to data objects in a test dataset. Here using diabetes dataset now a days the percentage of diabetes patient is growing very fast. India accounts for the largest number of people suffering from diabetes in the world. The diabetes in country's population is likely to be affected from the disease. It is estimated that every five person with diabetes will be an Indian. It means that India has highest number of diabetes in any one of the country in the world. The attributes predict whether a person having diabetes or not. 4.2.1 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. Using diabetic’s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease. It should be noted that the attributes used in our proposed method are those used for diagnosis of diabetes and are not direct indicators of heart disease. Each algorithm requires submission of data in a specified format.
Fig 4.6 Naive Bayes Diabetic Dataset Classifier Predicted Output
Fig 4.2 Random Forest Diabetic Dataset Classifier Predicted Output
5. EXPERIMENTATION & RESULTS 5.1 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.
4.2 PROPOSED METHODOLOGY Classification is the process of finding a model that TP+TN Accuracy= ...... 1 describes and distinguishes data classes or concepts, for the ( TP+ TN+FP+FN) able purpose of being to use the model to predict the class of Random Naive Bayes objects whose class label is unknown. It is a technique which Methods / Parameters J48 tree Forest Classifier is used to predict group membership for data instances. 768 768 768 Classification is a two step process, first, it builds Number of Instances classification model using training data. Every object of the Accuracy 82.3% 83.0% 84.8% dataset must be pre-classified and the second the model Table.5.1 Comparison Results generated in the preceding step is tested by assigning class 369
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.8 AUGUST 2016, IMPACT FACTOR:0.61 From the above table 5.1 shows the performance of naive bayes classifier. The fig.5.1 shows comparison graphical representation of methods. The method can over perform the traditional method with classify recall rate of 0.842.
Fig.5.1 Comparison Results 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 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. 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.
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