A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

  • R.S. PadmaPriya
  • P.Senthil Vadivu
Keywords: Association Rules, Classification, Decision Trees, Prediction, Recurrence

Abstract

The most common type of cancer in women worldwide is the Breast Cancer. Breast cancer may be detected early using Mammograms, probably before it's spread. Recurrent breast cancer could occur months or years after initial treatment. The cancer could return within the same place because the original cancer (local recurrence), or it may spread to different areas of your body (distant recurrence). Early stage treatment is done not only to cure breast cancer however additionally facilitate in preventing its repetition/recurrence. Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback. The projected analysis can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms. Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.

Downloads

Download data is not yet available.

References

Shelly Gupta, Dharminder Kumar, & Anand Sharma. (2011). Data mining classification techniques applied for breast cancer diagnosis and prognosis. Indian Journal of Computer Science and Engineering, 2(2), 188-195.

Ahmad. LG & Eshlagy. AT. (2013). Using three machine learning techniques for predicting breast cancer recurrence. Journal of Health and Medical Informatics, 4(2). Available at: https://www.researchgate.net/publication/259583297_using_three_machine_learning_techniques_for_predicting_breast_cancer.

Chintan. Shah & Anjali. G. Jivani. (2013). Comparison of data mining classification algorithms for breast cancer prediction. Fourth International Conference on Computing, Communications and Network Technologies, 1, 1-4

B.Padmapriya & T.Velmurugan. (2014). A survey on breast cancer analysis using data mining techniques. IEEE International Conference on Computational Intelligence and Computing Research. Available at: https://ieeexplore.ieee.org/document/7238530.

Murari Kumar, Shivkumar Singh Tomar, & Bhupesh Gaur. (2015). Mining based optimization for breast cancer analysis. International Journal of Computer Applications, 119(13), 1-6.

B. R. A. Cirkovic, A. M. Cvetkovic, S. M. Ninkovic, & D. Nenad. (2015). Prediction models for estimation of survival rate and relapse for breast cancer patients. IEEE International Conference on Bioinformatics and Bioengineering. DOI:10.1109/BIBE.2015.7367658

G. D. Rashmi, A. Lekha, & N. Bawane. (2015). Analysis of efficiency of classification and prediction algorithms (Naïve Bayes) for breast cancer dataset. International Conference on Emerging Research in Electronics, Computer Science and Technology, pp. 310–314.

A. I. Pritom. (2016). Predicting breast cancer recurrence using effective classification and feature selection technique. Available at: https://ijrier.com/publishe

d-papers/volume-3/issue-5/predicting-breast-cancer-recurrence-using-effective-classification-and-feature-selection-technique.pdf..

J. Diz, G. Marreiros & A. Freitas. (2016). Applying data mining techniques to improve breast cancer diagnosis. Journal of Medical Systems, 40(9). Available at: https://link.springer.com/journal/10916.

E. Aličković & A. Subasi. (2017). Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Computing and Applications, 28(4), 753–763.

J. Guo et al. (2017). Revealing determinant factors for early breast cancer recurrence by decision tree. Information Systems Frontiers, 19(6), DOI: https://doi.org/10.1007/s10796-017-9764-0.

R. J. Kate & R. Nadig. (2017). Stage-specific predictive models for breast cancer survivability. International Journal of Medical Informatics, 97, 304–311.

M. R. Mohebian, H. R. Marateb, M. Mansourian, M. A. Mañanas, & F. Mokarian. (2017). A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized ensemble learning. Computational and Structural Biotechnology Journal, 15, 75–85.

Akinsola Adeniyi F, Sokunbi M.A, Okikiola F.M, & Onadokun I.O. (2017). Data mining for breast cancer classification. International Journal of Engineering and Computer Science, 6(8), 22250-22258.

Nisreen I.R. Yassin, Shaimaa Omran, Enas M.F. El Houby, & Hemat Allam. (2018). Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Available at: https://europepmc.org/abstract/med/29428074.

Hamid Karim Khani Zand. (2015). A comparitive survey on data mining techniques for breast cancer diagnosis and prediction. Indian Journal of Fundamental and Applied Life Sciences, 5, 4330-4339.

Siyabend Turgut, Mustafa Datekin, & Tolga Ensari. (2018). Microarray breast cancer data classification using machine learning methods. IEEE Transactions on Computational Biology. Available at: https://www.semanticscholar.org/paper/Microarray-breast-cancer-data-classification-using-Turgut-Da%C4%9Ftekin/160fd6477a64ce89a65d74485b83cbe8a92efcf5.

Meriem Amrane, Saliha Oukid Ikram, & Gagaoua Tolga Ensar. (2018). Breast cancer classification using machine learning. IEEE Transactions on Computational Biology, 1-4.

Dona Sara Jacob, Rakhi Viswan, V Manju, L Padma Suresh, & Shine Raj. (2018). A survey on breast cancer prediction using data mining techniques. IEEE Conference on Emerging Devices and Smart Systems. Available at: http://toc.proceedings.com/41848webtoc.pdf.

Sapiah Sakri, Nuraini Abdul Rashid, & Zuhaira Muhammad Zain. (2017). Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access, pp 1-1. 10.1109/ACCESS.2018.2843443.

Amrita Sanjay, H Vinayak Nair, Sruthy Murali, & Krishnaveni K S. (2018). A data mining model to predict breast cancer using improved feature selection method on real time data. International Conference on Advances in Computing, Communication and Informatics. Available at: https://ieeexplore.ieee.org/document/8554450.

Published
2019-06-29
How to Cite
R.S. PadmaPriya, & P.Senthil Vadivu. (2019). A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence. International Journal of Engineering and Management Research, 9(3), 142-146. https://doi.org/10.31033/ijemr.9.3.06
Section
Articles