Abstract
Cancer is utmost dangerous disease that leads to death stage if not cured on time. Breast cancer is the second most common disease after lung cancer in women. Therefore, its early detection is of utmost importance. Machine learning plays an important role to predict breast cancer in the early stages. In this paper, the authors present a comparison study to predict breast cancer on the Breast Cancer Wisconsin Diagnostic dataset by applying six different machine learning algorithms such as CART, logistic regression, support vector classifier, hard voting classifier, Extreme Gradient Boosting, and artificial neural network. Authors have used various metrics for model evaluation keeping accuracy as one of the most important factors since higher accuracy models can help doctors to better detect the presence of breast cancer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics. CA Cancer J. Clinicians 69(1), 7–34 (2019)
A.-M. Noone, K.A. Cronin, S.F. Altekruse, N. Howlader, D.R. Lewis, V.I. Petkov, L. Penberthy, Cancer incidence and survival trends by subtype using data from the surveillance epidemiology and end results program, 1992–2013. Cancer Epidemiol. Prevent. Biomarkers 26(4), 632–641 (2017)
B. Settles, in Active learning literature survey. Technical Report, University of Wisconsin-Madison Department of Computer Sciences (2009)
M.R. Al-Hadidi, A. Alarabeyyat, M. Alhanahnah, Breast cancer detection using k-nearest neighbor machine learning algorithm, in 2016 9th International Conference on Developments in eSystems Engineering (DeSE) (Aug 2016), pp. 35–39
N. Khuriwal, N. Mishra, Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm, in 2018 IEEMA Engineer Infinite Conference (eTechNxT). IEEE (2018), pp. 1–5
S. Kharya, S. Agrawal, S. Soni, Naive Bayes classifiers: a probabilistic detection model for breast cancer. Int. J. Comput. Appl. 92(10), 0975–8887 (2014)
S.D. Narvekar, A. Patil, J. Patil, S. Kudoo, Prognostication of breast cancer using data mining and machine learning. Int. J. Adv. Res. Ideas Innov. Technol. 5(2), 921–924
L. Liu, Research on logistic regression algorithm of breast cancer diagnose data by machine learning, in 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE (2018), pp. 157–160
A.F.M. Agarap, On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset, in Proceedings of the 2nd International Conference on Machine Learning and Soft Computing (ACM, 2018), pp. 5–9
A. Lg, A.T. Eshlaghy, A. Poorebrahimi, M. Ebrahimi, R. Ar, Using three machine learning techniques for predicting breast cancer recurrence (2013)
S.A. Medjahed, T.A. Saadi, A. Benyettou, Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. Int. J. Comput. Appl. 62(1) (2013)
Prateek, Breast cancer prediction: importance of feature selection, in Advances in Computer Communication and Computational Sciences (Springer, 2019), pp. 733–742
I. Saritas, Prediction of breast cancer using artificial neural networks. J. Med. Syst. 36(5), 2901–2907 (2012)
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
L.E. Raileanu, K. Stoffel, Theoretical comparison between the gini index and information gain criteria. Ann. Math. Artif. Intell. 41(1), 77–93 (2004)
A. Antos, B. Kégl, T. Linder, G. Lugosi, Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. 3, 73–98 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Grover, A., Pradhan, N., Hemrajani, P. (2021). Comparison-Based Study to Predict Breast Cancer: A Survey. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing, vol 1189. Springer, Singapore. https://doi.org/10.1007/978-981-15-6067-5_61
Download citation
DOI: https://doi.org/10.1007/978-981-15-6067-5_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6066-8
Online ISBN: 978-981-15-6067-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)