Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Shweta Kharya, Sunita Soni
10.5120/ijca2016908023

Shweta Kharya and Sunita Soni. Article: Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection. International Journal of Computer Applications 133(9):32-37, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Shweta Kharya and Sunita Soni},
	title = {Article: Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {9},
	pages = {32-37},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

In this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes.

References

  1. Abdelghani,Bellaachia.,Erhan,Guven.2006. Predicting Breast Cancer Survivability Using Data Mining Techniques . Scientific data mining workshop in conjuction with SIAM conference on Data Mining.
  2. Chen,M., Han,J., and Yu,P. 1997. IEEE Trans. Knowledge and Data Eng.8(866) .
  3. Diana, D. 2009. Prediction of recurrent events in breast cancer using the Naive Bayesian Classification. Annals of University of Craiova, Math. Comp. Sci. 36(2):92-96 ISSN: 1223-6934.
  4. Harry, Z.,Shengli,S. 2004.Learning weighted Naive Bayes with accurate Ranking. 4th IEEE International Conference on Data Mining.567-570,ISBN-0-7695-2142-8.
  5. Item Intensities. Knowledge and Information Systems, 6(2):203–229.
  6. Kharya ,S.2012. Using data mining techniques for diagnosis and prognosis of cancer disease. International Journal of Computer Science, Engineering and Information Technology 2(2):55-66.
  7. Kharya, S., Agrawal, S., and Soni,S.2014. Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer. International Journal of Computer Applications (0975 – 8887) Volume 92 (10):26-31.
  8. Mannila, H.1996.Methods and problems in data mining..Proc. of Int. Conf. on Database Theory.
  9. Mohd,F.,Thomas,M.,2007.Comparison of different classification techniques using WEKA for Breast cancer.IFMBE proceedings 15:520-523.
  10. Perichinsky,G., and R, Garc´ıa-Mart´ınez.2000 .Proc. Workshop Comput. Sc. Researchers (La Plata University Press, Buenos Aires. 107
  11. Perichinsky,G., R, Garc´ıa-Mart´ınez., and A. Proto.2000 .Knowledge Discovery Based on Computational Taxonomy And Intelligent Data Mining, CD of the VI Comput. Sc. Argentinean Congr.
  12. Perichinsky,G., R,Garc´ıa-Mart´ınez., A, Proto., A,Sevetto., and D, Grossi.2001. Data Mining: Supervised and Non-Supervised Intelligent Knowledge Discovery, Proc. II Workshop Computes Sc. Researchers
  13. S, Aruna., Dr S.P. Rajagopalan .,and L.V. Nandakishore.2011. Knowledge based analysis of various Statistical tools in detecting breast Cancer.CCSEA. 02:37-45.
  14. Soni,S.,Vyas,O.P 2013.Building Weighted Associative Classifiers using Maximum Likelihood Estimation to Improve Prediction Accuracy in Health Care Data Mining..Journal of Information & Knowledge Management. 12(1) 1350008 (14 pages)
  15. Soni. J. Ansari. Uzma., Sharma, D., and Soni ,S.2011.Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers.IJCSE, 3(6),ISSN:0975-3397
  16. Wang W., Yang, J., and Yu, P.S. 2004. WAR: Weighted Association Rules for item intensities.
  17. Link1-Retreived from http://csc.liv.ac.uk/~frans/KDD/software/LUCS-KDD-DN/datasets/dataSet.html.
  18. Link-2 Retrieved from UCI Machine Learning Repository. [http://archive.ics.uci.edu/ml/]. Irvine, CA: University of California, Center for Machine Learning and Intelligent Systems.

Keywords

Data Mining, Breast cancer, Naive bayes classifier, Domain based weight, Weights, Posterior probability, UCI machine learning repository, Prediction.