Comparative Study and Analysis of Classification Algorithms In Data Mining Using Diabetic Dataset

Authors

  • R. S. Suryakirani  PG Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
  • R. Porkodi  Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Keywords:

Classification Algorithms, Naive Bayes, Random Tree, Decision Tree, J48.

Abstract

Classification is use to categorize each item in a set of data into one of predefined set of module or grouping. The data analysis task classification is where a model or classifier is constructed to predict categorical labels. The goal of the classification is to accurately predict the target class for each case in the data. The field of data mining due to its enormous success in terms of broad ranging application achievements and scientific progress, understanding. Many data mining application have been successfully implemented in various domains like healthcare, finance, retail, telecommunication, fraud detection and risk analysis etc. This paper presents the study and analysis of four classification algorithms namely J48, Random tree, Decision tree and Naive Bayes for Diabetic dataset and the performance are compared using the measures such as computing time, Correctly Classified Instances, Incorrectly Classified Instances, kappa statistics, Precision, Recall and F measure. The experimental result shows that J48 provides better accuracy than the Random tree, Decision tree and Naive Bayes.

References

  1. http://en.wikipedia.org/wiki/Data_mining
  2. http://www.flatworldsolutionscom/data-management/articles/data-mining -future- trends. php.
  3. D.Sindhuja R. Jemina Priyadarsini, International Journal of Computer Science and Mobile Computing" A Survey On Classification Techniques In Data Mining for Analysing Liver Disease Disorder".
  4. Geraldin B. Dela Cruz, Member, IACSIT, Bobby D. Gerardo, and Bartolome T. Tanguilig III" Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining"
  5. Abhale Babasaheb Annasaheb, Vijay kumarverma (2006)," Data Mining Classification Techniques: A Recent Survey “International Journal of Emerging Technologies in Engineering Research (IJETER).
  6. P.Yasodha -Pachiyappa's college for women, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, N.R. Ananthanarayanan Pachiyappa's college for women,Sri Chandra sekharendraSaraswathiViswaMahavidyalaya " Comparative Study of Diabetic Patient Data's Using Classification Algorithm in WEKA Tool.
  7. Shelly Gupta,DharmindarKumar,Anand Sharma," Indian Journal of Computer Science and Engineering (IJCSE)", Data Mining Classification Techniques Applied.For Breast Cancer Diagnosis And Prognosis.
  8. Sonali Agarwal, G. N. Pandey, and M. D. Tiwari, International Journal of e-Education, e-Business, e-Management and e-Learning," Data Mining in; Education: Data Classification and Decision Tree Approach".
  9. Mrs. M.S. Mythili1, Dr. A.R.Mohamed Shanavas2 IOSR Journal of Computer Engineering (IOSR-JCE)" An Analysis of students' performance using classification algorithms"
  10. DorinaKabakchieva, International Journal of Computer Science and Management Research," Students Performance Prediction By Using Data Mining Classification Algorithm".
  11. M. Sujatha,S. Prabhakar,Dr. G. Lavanya Devi , International Journal of Innovations in Engineering and Technology (IJIET “A Survey Of Classification Techniques In Data Mining".
  12. Hlaudi Daniel Masethe, Mosima Anna Masethe, Proceedings of the World Congress on Engineering and Computer Science 2014 Vol II " Prediction of Heart Disease using Classification Algorithms".
  13. Sagar S.Nikam,(),An international research journal of computer science and technology,"A comparative study of classification techniques in data mining algorithms".
  14. http://storm.cis.fordham.edu/~gweiss/data-mining/weka-data/diabetes.arff.
  15. R.Ranjani Rani,P.Manikandan,D.Ramya chitre,International Journal of Advanced Research in Computer Science, "An Emprical Analysis of Classification Tree Algorithm for Protein Datasets".
  16. Mats Jontell, Oral medicine, Sahlgrenska Academy,Göteborg University (1998) “A Computerised Teaching Aid in Oral Medicine and Oral Pathology. “ OlofTorgersson, department of Computing Science, Chalmers University of Technology, Göteborg.
  17. T. Mitchell, "Decision Tree Learning", in T. Mitchell, Machine Learning (1997) the McGraw- Hill Companies, Inc., pp. 52-78.
  18. Witten Ian H., E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Ch. 8, © 2000 Morgan Kaufmann Publishers
  19. http://grb.mnsu.edu/grbts/doc/manual/J48_Decision_Trees.html, accessed.
  20. Jiawei Han and Micheline KamberData Mining: Concepts and Techniques, 2ndedition.
  21. Baik, S. Bala, J. (2004), A Decision Tree Algorithm For Distributed Data Mining.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2018-02-28

Issue

Volume 4, Issue 2, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. S. Suryakirani, R. Porkodi, " Comparative Study and Analysis of Classification Algorithms In Data Mining Using Diabetic Dataset, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.299-304, January-February-2018.