Data mining techniques can be used by Health organizations to identify the diseases like heart, tumor, diabetic, liver and thyroid disease using symptoms as parameters. Diabetic disorder is one of the growing diseases worldwide currently faced by people because of modified life style. Valuable data can be observed from application of knowledge mining techniques in the fitness care system particularly in Diabetic Disease. In this direction, this research paper studies the performance of three classifier algorithms available, namely JRip, PART and Random Tree using WEKA tool and proposed a new algorithm Weighted Classifier to classify the data a diabetic data set. The objective of this research is to classify data, assist the people by extracting useful knowledge from classified data and identify the efficient algorithm to best prediction of disease. From the experimental analysis, it is concluded that weighted Classifier is the effective algorithm for classification accuracy. The result will help doctors in a diagnosis process.

Data mining, Diabetes disease, JRip, PART, Random Tree, Weighted Classifier, classification, WEKA tool

[1] P. Yasodha , M. Kannan M, “Analysis of Population of Diabetic Patient Database in WEKA Tool”, International Journal of Science and Engineering Research, VoL.2 Issue.5, 2011.
[2] S. Vijayarani , S. Sudha, “Comparative Analysis of Classification Function Techniques for Heart Disease Prediction”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.1, Issue.3, pp.735-741, 2013.
[3] Dr.V.Karthikeyani , I.Parvin Begum “Comparison a Performance of Data Mining Algorithms (CPDMA) in Prediction Of Diabetes Disease” International Journal on Computer Science and Engineering (IJCSE) Vol. 5 No. 03 Mar 2013 205-210
[4] S. Tirunagari, N. Poh, K.Aliabadi, D.Windridge & D.Cooke, “Patient level analytics using self-organising maps: A case study on Type-1 Diabetes self-care survey responses”. In Computational Intelligence and Data Mining (CIDM), IEEE Symposium on pp. 304-309, 2014.
[5] N. Singh, A. Jindal, “A Segmentation Method and Comparison of Classification Methods for Thyroid Ultrasound Images”, International Journal of Computer Applications, Vol.50, Issue.11, 2012.
[6] D.R.Adidela , D.G.Lavanya ,S.G. Jaya,A.R. Allam , “Application of fuzzy ID3 to predict diabetes”. International Journal Advance Computer Mathematics Science, Vol.3, Issue.4, pp.541, 2012.
[7] M. Durairaj ,G. Kalaiselvi , “ Prediction Of Diabetes Using Soft Computing Techniques- A Survey”, International Journal of Scientific & Technology Research, Vol.4, Issue.3, 2015.
[8] A. Rajput, R.P.Aharwal, M. Dubey, S.P. saxena “J48 and JRIP Rules for E-Governance Data” International Journal of Computer Science and Security, Vol.5, Issue.2, pp.201-207, 2011.
[9] E. Frank, Ian H. Witten, “Generating Accurate Rule Sets Without Global Optimization”. In Fifteenth International Conference on Machine Learning, pp.144-151, 1998.
[10] M. H. Danham, S.Sridhar,” Data mining, Introductory and Advanced Topics”, Pearson education, 1st ed., 2006.
[11] R. Remco, Bouckaert, E. Frank, M. Hall, R. kirkby, P.Reutemann, A.Seewald, D. Scuse, “WEKA Manual for Version 3-7-5”, 2011.
[12] Dr. V. Karthikeyini, I. Parvin Begum,” Comparison a Performance of Data Mining Algorithms (CPDMA) in Prediction Of Diabetes Disease”, International Journal on Computer Science and Engineering (IJCSE), Vol.5 Issue.3, 2013.
[13] P.P.Dhakate, S. Patil, K. Rajeswari, D.Abin “Preprocessing and Classification in WEKA Using Different Classifier”, International Journal of Engineering Research and Applications, Vol.4, Issue.8, pp.91-93, 2014
[14] I.Parvin begum, V. Karthikeyini, K. Tajuddin, I. Shahina Begum, “Comparative of data mining classification algorithm (CDMCA) in Diabetes Disease Prediction”, International journal of Computer Applications, Vol.60, Issue.12, pp. 26-31, 2012.
[15] K. Rajesh , V. Sangeetha, “Application of data mining methods and techniques for diabetes diagnosis” . International Journal of Engineering and Innovative Technology (IJEIT), Vol.2,Issue.3, pp.224.