CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer

by Shweta Kharya, Shika Agrawal, Sunita Soni
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 10
Year of Publication: 2014
Authors: Shweta Kharya, Shika Agrawal, Sunita Soni
10.5120/16045-5206

Shweta Kharya, Shika Agrawal, Sunita Soni . Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer. International Journal of Computer Applications. 92, 10 ( April 2014), 26-31. DOI=10.5120/16045-5206

@article{ 10.5120/16045-5206,
author = { Shweta Kharya, Shika Agrawal, Sunita Soni },
title = { Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 10 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number10/16045-5206/ },
doi = { 10.5120/16045-5206 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:56.394312+05:30
%A Shweta Kharya
%A Shika Agrawal
%A Sunita Soni
%T Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 10
%P 26-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Naive Bayes is one of the most effective statistical and probabilistic classification algorithms. As health care environment is "information loaded" but "knowledge deprived". So to extract knowledge, effective analysis tools are constructed to discover hidden relationships in data. The aim of this work is to design a Graphical User Interface to enter the patient screening record and detect the probability of having Breast cancer disease in women in her future using Naive Bayes Classifiers, a Probabilistic Classifier. As breast cancer is considered to be second leading cause of cancer deaths in women today so early detection can improve the survival rate of women. The prediction is performed from mining the patient's historical data or data repository. Further from the experimental results it has been found that Naive Bayes Classifiers is providing improved accuracy with low computational effort and very high speed. The system has been implemented using java platform and trained using benchmark data from UCI machine learning repository. The system is expandable for the new dataset.

References
  1. Diana Dumitru "Prediction of recurrent events in breast cancer using the Naive Bayesian Classification" Annals of University of Craiova, Math. Comp. Sci. Ser. Volume 36(2), 2009, Pages 92-96 ISSN: 1223-6934.
  2. Mohd Fauzi bin Othman,Thomas Moh Shan Yau,"Comparison of Different Classification Techniques Using WEKA for Breast Cancer ",IFMBE Proceedings 15, pp. 520-523, 2007.
  3. S. Aruna, Dr S. P. Rajagopalan , L. V. Nandakishore, "Knowledge based analysis of various Statistical tools in detecting breast Cancer",CCSEA 2011,CS & IT 02,pp. 37-45,2011.
  4. K. Usha Rani,"Parallel Approach for Diagnosis of Breast Cancer using Neural Network Technique", International Journal of Computer Applications (0975 – 8887) Volume 10– No. 3, November 2010.
  5. G. Perichinsky and R. Garc´?a-Mart´?nez, Proc. Workshop Comput. Sc. Researchers (La Plata University Press, Buenos Aires, 2000), p. 107.
  6. G. Perichinsky, R. Garc´?a-Mart´?nez and A. Proto, Knowledge Discovery Based on Computational Taxonomy And Intelligent Data Mining, CD of the VI Comput. Sc. Argentinean Congr. (Ushuaia, 2000).
  7. G. Perichinsky, R. Garc´?a-Mart´?nez, A. Proto, A. Sevetto and D. Grossi, Data Mining: Supervised and Non-Supervised Intelligent Knowledge Discovery, Proc. II Workshop Computes Sc. Researchers (San Luis University Press, San Luis, 2001).
  8. G. Perichinsky, A. Servetto, R. Garc´?a-Mart´?nez, R. Orellana and A. Plastino, Tax-omic Evidence Applying Algorithms of Intelligent Data Minning Asteroid Families,comput. Sci. , Software Eng. , Information Technology, e-Bussines & Applications (Rio de Janeiro, 2003), p. 308.
  9. M. Chen, J. Han and P. Yu, IEEE Trans. Knowledge and Data Eng. 8, 866 (1996).
  10. H. Mannila, Methods and problems in data mining, Proc. of Int. Conf. on Database Theory (Delphi, Greece, 1997).
  11. G. Piatetski-Shapiro, W. J. Frawley and C. J. Matheus, Knowledge Discovery in Databases: An Overview (AAAI-MIT Press, Menlo Park, California, 1991).
  12. S. Evangelos and J. Han, Proc. 2nd Int. Conf. Knowledge Discovery and Data Min. (Portland, United States, 1996).
  13. R. S. Michalski, J. G. Carbonell and T. M. Mitchell, Machine learning I: An AI Approach (Morgan Kaufmann, Los Altos, CA, 1983).
  14. M. Holsheimer and A. Siebes, Data mining: The search for knowledge in databases,Report CS-R9406 (University of Amsterdam, Amsterdam, 1991).
  15. S. kharya ," Using data mining techniques for diagnosis and prognosis of cancer disease" International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 2, No. 2, April 2012
  16. http://csc. liv. ac. uk/~frans/KDD/software/LUCS-KDD-DN/datasets/dataSet. html.
  17. UCI Machine Learning Repository. [http://archive. ics. uci. edu/ml/]. Irvine, CA: University of California, Center for Machine Learning and Intelligent Systems.
  18. Jyoti Soni et. al ,"Intelligent and effective Heart Disease Prediction System using Weighted Associative Classifiers" International Journal on Computer Science and Engineering,Vol. 3 No. 6,ISSN:0975-3397,June 2011.
  19. Gouda I. Salama1 et. al," Breast Cancer Diagnosis on Three Different Datasets Using Multi- Classifiers", International Journal of Computer and Information Technology (2277 – 0764) Volume 01– Issue 01, September 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Breast Cancer Naive Bayes Classifiers UCI machine learning repository Prediction Posterior probability Accuracy.

Powered by PhDFocusTM