Open Access   Article Go Back

Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning

Pushpraj Saket1 , Anshul Khurana2

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-10 , Page no. 149-151, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si10.149151

Online published on May 05, 2019

Copyright © Pushpraj Saket, Anshul Khurana . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Pushpraj Saket, Anshul Khurana, “Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.149-151, 2019.

MLA Style Citation: Pushpraj Saket, Anshul Khurana "Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning." International Journal of Computer Sciences and Engineering 07.10 (2019): 149-151.

APA Style Citation: Pushpraj Saket, Anshul Khurana, (2019). Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning. International Journal of Computer Sciences and Engineering, 07(10), 149-151.

BibTex Style Citation:
@article{Saket_2019,
author = {Pushpraj Saket, Anshul Khurana},
title = {Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {10},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {149-151},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=992},
doi = {https://doi.org/10.26438/ijcse/v7i10.149151}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.149151}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=992
TI - Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning
T2 - International Journal of Computer Sciences and Engineering
AU - Pushpraj Saket, Anshul Khurana
PY - 2019
DA - 2019/05/05
PB - IJCSE, Indore, INDIA
SP - 149-151
IS - 10
VL - 07
SN - 2347-2693
ER -

           

Abstract

Breast cancer is one amongst the leading cancers for ladies in developed countries including Asian nation .It is the second most typical explanation for cancer death in women. The high incidence of breast cancer in women has redoubled considerably within the last years. Naïve Bayes algorithm is used for carcinoma identification Prognosis and diagnosis. Carcinoma Diagnosis is identifying of benign from malignant breast lumps and carcinoma Prognosis predicts once Breast Cancer is to recur in patients that have had their cancers excised. In this paper Naïve Bayes Algorithm is used to classify the Datasets of Breast Cancer (Diagnosis). The classification results show that when two features of maximum radius and maximum texture is selected, the classification improved accuracy is 98.6%, which is improved compared with previous method.

Key-Words / Index Term

Breast Cancer Dataset, NaïveBayes classification Algorithm

References

[1] Abdelghani Bellaachia, Erhan Guven, “Predicting Breast Cancer Survivability Using Data Mining Techniques”, The George Washington University, Washington DC 20052
[2] Shweta 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
[3] G. Ravi Kumar, Dr. G. A. Ramachandra, K.Nagamani, “An Efficient Prediction of Breast Cancer Data using Data Mining Techniques”, International Journal of Innovations in Engineering and Technology (IJIET)
[4] A.PRIYANGA, Dr.S.PRAKASAM, “The Role of Data Mining-Based Cancer Prediction system (DMBCPS) in Cancer Awareness”, International Journal of Computer Science and Engineering Communications- IJCSEC. Vol.1 Issue.1, December 2013
[5] Shelly Gupta, Dharminder Kumar, Anand Sharma “Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis”, Indian Journal Of Computer Science And Engineering (Ijcse)
[6] Sarvestan Soltani A. , Safavi A. A., Parandeh M. N. and Salehi M., “Predicting Breast Cancer Survivability using data mining techniques,” Software Technology and Engineering (ICSTE), 2nd International Conference, 2010, vol.2, pp.227-231.
[7] Anunciacao Orlando, Gomes C. Bruno, Vinga Susana, Gaspar Jorge, Oliveira L. Arlindo and Rueff Jose, “A Data Mining approach for detection of high-risk Breast Cancer groups,” Advances in Soft Computing, vol. 74, pp. 43-51, 2010.
[8] Abdelaal Ahmed Mohamed Medhat and Farouq Wael Muhamed, “Using data mining for assessing diagnosis of breast cnacer,” in Proc. International multiconfrence on computer science and information Technology, 2010, pp. 11-17.