ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING
JULY 2016 VOL- 6 NO - 7
NOVEMBER 2015 VOL- 5 NO - 11
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL. 6 NO.7 JULY 2016 IMPACT FACTOR: 0.61
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: email@example.com 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. 6 NO.7 JULY 2016 IMPACT FACTOR: 0.61
International Journal of Innovative Technology & Creative Engineering Vol.6 No.7 July 2016
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL. 6 NO.7 JULY 2016 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. Knuckleballs baffle baseball players with their unpredictable swerves. A new study suggests a possible cause of the pitchâ€™s erratic flight sudden changes in the drag force on a ball, due to a phenomenon called a drag crisis. The result is at odds with previous research that attributed the zigzags to the effect of airflow over the baseballâ€™s seams. Scientists report the finding July 13 in the New Journal of Physics. Knuckleballs are well known in baseball, but similar phenomena also confound players in soccer and volleyball. Knuckleballs occur when balls sail through the air with very little spin, producing unstable flight. In drag crisis, the thin layer of air surrounding the ball flips between turbulent and smooth flow, abruptly changing the drag forces on the ball. If the transition occurs asymmetrically, it can push the ball to one side. Balls must move at a certain speed to experience a drag crisis, which may be why knuckleballs tend to be thrown slower than other pitches, the researchers suggest. While the fastest pitches can top 100 miles per hour, knuckleballs are usually closer to 60 or 70 miles per hour. Every day, millions of Americans with diabetes have to inject themselves with insulin to manage their bloodsugar levels. But less painful alternatives are emerging. Scientists are developing a new way of administering the medicine orally with tiny vesicles that can deliver insulin where it needs to go without a shot. Today, they share their in vivo testing results. The researchers are presenting their work at the 252nd National Meeting & Exposition of the American Chemical Society (ACS). We have developed a new technology called a Cholestosome. A Cholestosome is a neutral, lipid-based particle that is capable of doing some very interesting things. The biggest obstacle to delivering insulin orally is ushering it through the stomach intact. Proteins such as insulin are no match for the harsh, highly acidic environment of the stomach. They degrade before they get a chance to move into the intestines and then the bloodstream where they're needed. 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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Contents A Novel Technique for Prediction of Diabetic Patients Data Using Naives Bayes Classification in WEKA Tool S.Nivetha & Dr.A.Geetha ……………….…………………………………….
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.7 JULY 2016, IMPACT FACTOR:0.61
A Novel Technique for Prediction of Diabetic Patients Data Using Naives Bayes Classification in WEKA Tool S.Nivetha Assistant Professor, Department of Computer Science, Kamban Arts and Science College, Pollachi, Tamil Nadu, India. Email: firstname.lastname@example.org Dr.A.Geetha Assistant Professor, PG & Research Department of Computer Science, Chikkanna Govt. Arts College, Tirupur, Tamil Nadu, India. Email: email@example.com Abstract - Data mining is a process of extracting information from a dataset and transform it into understandable structure for further use, also it discovers patterns in large data sets. Data mining has number of techniques such as pre-processing, classification. Classification is a technique used for predicting group for the diabetic dataset instance. In this paper, classification on diabetes database are developed classifiers are compared with the result based on certain parameters using WEKA tool. India is suffering from diabetes patients of the population with equal rates in both women and men resulted in deaths with worldwide. A comparative analysis has been performed with the classifiers which result in the chance of diabetic patients getting heart disease. The performances compared with precision, recall, F-measure, Kappa statistic, root mean square and time seconds build the model has exhibited a great overall performance. Keywords- Data Mining, Diabetic Data, RF, NB, WEKA.
1. INTRODUCTION Today, information has a great value and the amount of information has been expansively growing during last few years. Especially, text databases are rapidly growing due to the increasing amount of information available in electronic forms. Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. That type of Information can be used to increase revenue, cuts costs or both. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. 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. Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. 2. REVIEW OF LITERATURE In this research work, discussed about the methodology for feature extraction and document classification. In order to propose this works are have analyzed various Literatures
which are very much relevant and helpful to do this work. The literature where are have retrieved and analyzed are presented in the following section. Thangaraju  et al., proposed a preclusion and discovery of skin melanoma risk using clustering techniques. The skin melanoma patients data are gathered from different diagnostic centre which contains both cancer and non-cancer patientâ€™s information. The gathered data are pre-processed and then clustered using K-means algorithm for separating relevant and non- relevant data to skin melanoma. It is implemented in c#.net to predict skin melanoma risk level with suggestions which is easier, cost reducible and time savable. Mohd Fauzi bin Othman  et al., examined the performance of different classification and clustering methods for a set of bulk data. The algorithm and methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. Bharat Chaudharil  et al., analyzed the comparison of three major clustering algorithms are K- Means, Hierarchical clustering and Density based clustering algorithm. The performances of these three algorithms are compared based on the feature of correctly class wise clustering. The performance of these three clustering algorithms is compared using a Data mining tool WEKA. Amandeep Kaur Mann  et al., presented a clustering and its different techniques in data mining is done. Kawsar Ahmed  et.al proposed a system to detect the Lung cancer risk. Their proposed system was easy, cost effective and time saving. The data are collected from different diagnosis centres. The collected data are pre-processed and clustered using K-means algorithm. Then Apriori and Decision tree algorithm are used to find significant frequent pattern. Then they developed a significant frequent pattern tool for lung cancer prediction system. Rajalingam  et al., presented a comparative study of implementation of hierarchical clustering algorithms agglomerative and divisive clustering for various attributes. The Visual Programming Language is used for implementation of these algorithms. The result of this paper study is the performance of divisive algorithm works as twice as fast as the agglomerative algorithm.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.7 JULY 2016, IMPACT FACTOR:0.61 Khaled Hammouda  et al., presented the reviews of four off-line clustering algorithms are K-means clustering, Fuzzy C-means clustering, Mountain clustering, and Subtractive clustering. The algorithms are implemented and tested against a medical problem of heart disease diagnosis. The accuracy and performance are compared. Aastha Joshi  et al., proposed a brief review of six different types of clustering techniques are K-means clustering, Hierarchical clustering, DBSCAN clustering, OPTICS, and STING. Manish Verma et al.,  proposed a analysis of six types of clustering techniques are k-Means Clustering, Hierarchical Clustering, DBSCAN clustering, Density Based Clustering, Optics and EM Algorithm. WEKA tool is used for implemented and analyzed. Shraddha K.Popat et.al  focused on different clustering techniques. They are Partition algorithms, Hierarchical algorithms, Density based clustering algorithm. The result was hierarchical clustering can be perform better than the other techniques. Pradeep Rai et al.,  presented a survey is to provide a comprehensive review of different clustering techniques in data mining.
Fig 3.2 Diabetic Attributes Selected in WEKA 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.
3. DATA MINING TOOL An open-source development model usually means that the tool is a result of a community effort, not necessary supported by a single institution but instead the result of contributions from an international and informal development team. This development style offers a means of incorporating the diverse experiences. The open source tools available for data mining. 3.1 WEKA Waikato Environment for Knowledge Analysis. Weka is a collection of machine learning algorithms for data mining tasks. These algorithms can either be applied directly to a data set or can be called from your own Java code. Weka contains a collection of several tools for visualization and algorithms for analytics of data and predictive modeling, together with graphical user interfaces for easy access to this functionality. Fig 4.1 J48 Diabetic Dataset Classifier Output
Fig 3.1 Explorer in WEKA Fig 4.2 J48 Diabetic Dataset Classifier Accuracy
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.7 JULY 2016, IMPACT FACTOR:0.61 4.1.2 Random Forest Random forest is an algorithm that consists of many decision trees. It was first developed by Leo Breiman and Adele Cutler [Bre01]. The idea behind it is to build several trees, to have the instance classified by each tree, and to give a vote at each class. 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 predictionâ€™s accuracy, a bootstrap method is used to create different trees. Every time a tree is created, one-third of the bootstrap sample is kept aside to estimate the test error. The subset that is not included in the tree construction is used as a test set for the same tree. Once a tree is built, it is used to predict all data and this allows computing proximities. Every time two instances fall into the same node, it increases the proximities. This computation is done for each tree and proximities can be used to replace missing values.
Fig 4.3 Random Forest Diabetic Dataset Classifier Output
4.2 PROPOSED METHODOLOGY Classification is the process of finding a model that describes and distinguishes data classes or concepts, for the able purpose of being to use the model to predict the class of objects whose class label is unknown. It is a technique which is used to predict group membership for data instances. Classification is a two step process, first, it builds classification model using training data. Every object of the dataset must be pre-classified and the second the model generated in the preceding step is tested by assigning class 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. It assumes that the presence or absence of particular feature of a class is unrelated to the presence or absence of any other feature. It is based on conditional probabilities. It uses Bayes' theorem which finds the probability of an event occurring, given the probability of another event that has already occurred. An advantage of the Naive Bayes classifier is that it requires only a small amount of training data to estimate the parameters necessary for classification. 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 diabeticâ€™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 4.4 Random Forest Diabetic Dataset Classifier Accuracy
Fig 4.6 Naive Bayes Diabetic Dataset Classifier Accuracy 365
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.7 JULY 2016, IMPACT FACTOR:0.61 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. Sensitivity= Specificity=
True Positive Rate ...... 1 ( True Positive + False Negative)
True Negative Rate ...... 2 ( False Positive + True Negative)
5.2 False Positive Rate The ratio of false positives to false positive plus true negatives. It can be defined as False Positive Rate=
False Positive ...... 3 ( False Positive + True Negative)
5.3 Precision Precision is calculated as number of correctly classified instances can be defined as Precision=
True Positive ...... 4 ( True Positive + False Positive)
5.4 F-Measure F-measure is combining recall and precision scores into a single measure of performance. F-Measure=
2*recall*precision ...... 5 (recall + precision) J48 tree
Naive Bayes Classifier
TP Rate FP Rate Precision Recall
0.936 0.336 0.839 0.936
1.000 0.001 1.000 1.000
0.842 0.384 0.803 0.842
Mean absolute error
Root mean squared Time taken
0.3452 0.02 sec
0.1506 0.53 sec
0.4133 0.01 sec
Methods / Parameters Number of Instances
Table.5.1 Comparison Results 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 plays a important role in extracting the hidden information in the diabetic data base. The preprocessing is used in order to improve the quality of
the data. This model is built based as a test case on the diabetic dataset. The experiment has been successfully performed with several data mining classifier techniques and naives bayes gives a better performance than other existing methods. To improve the quality of health care of diabetes patients can be implemented using WEKA tool. In this paper, considered time taken to build a model by the algorithms as a parameter. REFERENCES 1. P.Thangaraju, B.Deepa, “A Case study on Perclusion and Discovery of Skin Melanoma Risk using Clustering Techniques”, International Journal of Advanced Research in Electronics & Communication Engineering, Vol.3,Iss.7, 2014. 2. Mohd Fauzi bin Othman, Thomas Moh Shan Yau, “Comparison ofDifferent Classification Techniques using WEKA for BreastCancer”, F.Ibrahim, N.A. Abu Osman, J. Usman and N.A. Kadri: Biomed 06, IFMBE Proceedings 15, pp.520-523, 2007. 3. Bharat Chaudharil, Manan Parikh, “A Comparative Study of clustering algorithms using weka tools”, International Journal of Application or Innovation in Engineering & Management, Volume 1, Issue 2, 2012. 4. Amandeep Kaur Mann and Navneet Kaur, “Survey Paper on Clustering Techniques”, International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 4, 2013. 5. Khaled Hammouda, Prof. Fakhreddine Karray, “A Comparative Study of Data Clustering Techniques”, University of Waterloo, Ontario, Canada. 6. N. Rajalingam, K. Ranjini, “Hierarchical Clustering Algorithm, A Comparative Study”, International Journal of Computer Applications, Vol.19, No 3, 2011. 7. Aastha Joshi, Rajneet Kaur,“A Review: Comparative Study of Various Clustering Techniques in Data Mining”, International journal of Advanced Research in Computer Science & Software Engineering, Vol.3, Iss.3, 2013. 8. Manish Verma, “A Comparative Study of Various Clustering Algorithms in Data Mining” International Journal of Engineering Research and Applications, Vol. 2, Issue 3, pp.1379-1384,2012. 9. Shraddha K. Popat , “Review and Comparative Study of Clustering Techniques” International Journal of Computer Science & Information Technologies,Vol.5(1), 805-812, 2014. 10. K. Ruth Ramya, “A Class Based Approach for Medical Classification of Chest Pain”, International Journal of Engineering Trends and Technology, Vol. 3, Issue.2, pp.89-93, 2012. 11. Ru Xu, “Survey of Clustering Algorithms”, IEEE Transactions on Neural Networks, Vol.16, No 3, 2005. 12. A.Geetha,G.M.Nasira, “Rainfall Prediction using Logistic Regression Technique”,CiiT International Journal of Artficial Intelligence Systems and Machine Learning,Vol.6,No.7,pp.246-250,2014. 13. Sripriya, A.Geetha, “Cyclone Storm Prediction using KNN Algorithm”, Indian Journal of Engineering, Discovery Publication,12(30), pp.350-354,2015.
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International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711)