Predictive Analysis Of Breast Cancer Using Machine Learning Techniques
This paper is a product of the research Project “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques” performed in Manav Rachna International Institute of Research and Studies, Faridabad in the year 2018.
Introduction: The present article is part of the effort to predict breast cancer which is a serious concern for women’s health.
Problem: Breast cancer is the most common type of cancer and has always been a threat to women’s lives. Early diagnosis requires an effective method to predict cancer to allow physicians to distinguish benign and malicious cancer. Researchers and scientists have been trying hard to find innovative methods to predict cancer.
Objective: The objective of this paper will be predictive analysis of breast cancer using various machine learning techniques like Naïve Bayes method, Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine method.
Methodology: Predictive data mining has become an instrument for scientists and researchers in the medical field. Predicting breast cancer at an early stage helps in better cure and treatment. KDD (Knowledge Discovery in Databases) is one of the most popular data mining methods used by medical researchers to identify the patterns and the relationship between variables and also helps in predicting the outcome of the disease based upon historical data of datasets.
Results: To select the best model for cancer prediction, accuracy of all models will be estimated and the best model will be selected.
Conclusion: This work seeks to predict the best technique with highest accuracy for breast cancer.
Originality: This research has been performed using R and the dataset taken from UCI machine learning repository.
Limitations: The lack of exact information provided by data.
How to Cite
License
Copyright (c) 2019 Ingeniaría Solidaria
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cession of rights and ethical commitment
As the author of the article, I declare that is an original unpublished work exclusively created by me, that it has not been submitted for simultaneous evaluation by another publication and that there is no impediment of any kind for concession of the rights provided for in this contract.
In this sense, I am committed to await the result of the evaluation by the journal Ingeniería Solidaría before considering its submission to another medium; in case the response by that publication is positive, additionally, I am committed to respond for any action involving claims, plagiarism or any other kind of claim that could be made by third parties.
At the same time, as the author or co-author, I declare that I am completely in agreement with the conditions presented in this work and that I cede all patrimonial rights, in other words, regarding reproduction, public communication, distribution, dissemination, transformation, making it available and all forms of exploitation of the work using any medium or procedure, during the term of the legal protection of the work and in every country in the world, to the Universidad Cooperativa de Colombia Press.
C. E. Fear, et al., “Confocal microwave imaging for breast cancer detection: Localization of tumors in three dimensions,” IEEE Transactions on biomedical engineering, vol. 49, no. 8, pp. 812-822, 2002. http://dx.doi.org/10.1109/TBME.2002.800759
N. K. Nikolova, “Microwave imaging for breast cancer,” IEEE microwave magazine, vol. 12, no. 7, pp. 78-94, 2011. doi: http://dx.doi.org/10.1109/MMM.2011.942702
Xie, Yao, et al., “Multistatic adaptive microwave imaging for early breast cancer detection,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 8, pp. 1647-1657, 2006. doi: http://dx.doi.org/10.1109/TBME.2006.878058
E. J. Bond, et al., “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Transactions on Antennas and Propagation, vol. 51, no. 8, pp. 1690-1705, 2003. doi: http://dx.doi.org/10.1109/TAP.2003.815446
J. L. Kelsey, D. G. Marilie, and M. J. Esther, “Reproductive factors and breast cancer,” Epidemiologic reviews, vol. 15, no.1, p. 36, 1993. doi:http://dx.doi.org/10.1093/oxfordjournals.epirev.a036115
P. A. Francis, et al., “Adjuvant ovarian suppression in premenopausal breast cancer,” New England Journal of Medicine, vol. 372, no. 5, pp. 436-446, 2015. doi: http://dx.doi.org/10.1056/NEJMoa1412379
C. E. De Santis, et al., “Breast cancer statistics, 2015: Convergence of incidence rates between black and white women,” CA: a cancer journal for clinicians, vol, 66, no. 1, pp. 31-42, 2016. doi: http://dx.doi.org/10.3322/caac.21320
C. E. De Santis, et al., “Breast cancer statistics, 2017, racial disparity in mortality by state,” CA: a cancer journal for clinicians, vol. 67, no. 6, pp. 439-448, 2017. doi: http://dx.doi.org/10.3322/caac.21412
T. J. Whelan, et al., “Regional nodal irradiation in early-stage breast cancer,” New England Journal of Medicine, vol. 373, no. 4, pp. 307-316, 2015. doi: http://dx.doi.org/10.1056/NEJMoa1415340
K. C. Oeffinger, et al., “Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society,” Jama, vol. 314, no. 15, pp. 1599-1614, 2015. doi:http://dx.doi.org/10.1001/jama.2015.12783
M. Kan, et al., “Multi-view discriminant analysis,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 1, pp. 188-194, 2015. doi: http://dx.doi.org/10.1109/TPAMI.2015.2435740
M. Batra, and R. Agrawal, “Comparative Analysis of Decision Tree Algorithms,” in B. Panigrahi, M. Hoda, V. Sharma, and S. Goel (eds), Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652, pp. 1-4 Springer, Singapore, 2018. doi: http://dx.doi.org/10.1007/978-981-10-6747-1_4
R. Agrawal, “Design and development of data classification methodology for uncertain data,” Indian Journal of Science and Technology, vol. 9, no. 3, pp. 1-12, 2016. doi: http://dx.doi.org/10.17485/ijst/2016/v9i3/72262
R. Agrawal, “Integrated Parallel K-Nearest Neighbor Algorithm,” Smart Intelligent Computing and Applications, Springer, Singapore, pp. 479-486, 2019. doi: https://doi.org/10.1007/978-981-13-1921-1_47
Agrawal, Rashmi, http://hdl.handle.net/10603/169657 , p 1-50 https://shodhganga.inflibnet.ac.in/handle/10603/169657
Biau, Gérard, and Erwan Scornet. "A random forest guided tour." Test 25.2 , 197-227.,2016, 10.1007/s11749-016-0481-7, http://www.lsta.upmc.fr/BIAU/test-bs.pdf
American Cancer Society. (2019). Cancerorg. Accessed 11 July 2019. [Online]. Available from: https://www.cancer.org/cancer/breast-cancer/non-cancerous-breast-conditions.html
Nihgov. (2019). PubMed Central (PMC). Accessed 11 July 2019. [Online]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4115707/
R. Agrawal, “Integrated Effect of Nearest Neighbors and Distance Measures in k-NN Algorithm,” in V. Aggarwal, V. Bhatnagar, and D. Mishra (eds), Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654, pp. 1-6 Springer, Singapore, 2018. doi: https://doi.org/10.1007/978-981-10-6620-7_74
R. Agrawal, “A modified K-nearest neighbor algorithm using feature optimization,” Int. J. Eng. Technol., vol. 8, no. 1, pp. 28–37, 2016. [Online]. Available from: http://www.enggjournals.com/ijet/vol8issue1.html
