Abstract
As much as data science is playing a pivotal role everywhere, health care also finds its prominent application. Breast Cancer is the top-rated type of cancer amongst women; which alone took away 627,000 lives. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-the-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as an experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M1, Decision Table, J-Rip, J48, Lazy IBK, Lazy K-star, Logistics Regression, Multiclass Classifier, Multilayer–Perceptron, Naïve Bayes, Random Forest, and Random Tree is analyzed on this data set.
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Abbreviations
- MAE:
-
Mean absolute error
- RMSE:
-
Root mean squared error
- RAE:
-
Relative absolute error
- RRSE:
-
Root relative squared error
- TP:
-
True Positive
- TN:
-
True Negative
References
The Cancer Atlas, http://canceratlas.cancer.org/the-burden/breast-cancer/
American Institute of Cancer Research Statistics, https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics
World Health Organization Cancer Report, https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/
V. Kumar, P. Tiwari, B.K. Mishra, S. Kumar, Implementation of n-gram methodology for rotten tomatoes review dataset sentiment analysis. Int. J. Knowl. Discov. Bioinform (IJKDB). 7(1), 30–41 (2017). https://doi.org/10.4018/ijkdb.2017010103
V. Kumar, A. Verma, N. Mittal, S.V. Gromov, Anatomy of preprocessing of big data for monolingual corpora paraphrase extraction: source language sentence selection, in Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol. 814 (Springer Nature, Singapore, 2019), pp. 495–505. https://doi.org/10.1007/978-981-13-1501-5_43
V. Kumar, D. Kalitin, P. Tiwari, Unsupervised learning dimensionality reduction algorithm PCA for face recognition, in IEEE Xplore: International Conference on. Computing, Communication and Automation (ICCCA) (2017), pp. 32–37. https://doi.org/10.1109/ccaa.2017.8229826
V. Kumar, R. Zinovyev, A. Verma, P. Tiwari, Performance evaluation of lazy and decision tree classifier: a data mining approach for global celebrity‘s death analysis. IEEE Xplore: In International Conference on Research in Intelligent and Computing in Engineering (RICE) (2018), pp. 1–6, https://doi.org/10.1109/rice.2018.8509082
V. Kumar, M. Mazzara, A. Messina, J.Y. Lee, A conjoint application of data mining techniques for analysis of global terrorist attacks, prevention and prediction for combating terrorism, in Proceedings of 6th International Conference in Software Engineering for Defense Applications- SEDA 2018. Advances in Intelligent Systems and Computing, vol. 925 (Springer Nature, Switzerland, 2019), pp. 1–13. https://doi.org/10.1007/978-3-030-14687-0_13
V. Chaurasia, S. Pal, B.B. Tiwari, Prediction of benign and malignant breast cancer using data mining techniques. J. Algorithms Comput. Technol. 12(2), 119–126 (2018). http://dx.doi.org/10.1177/1748301818756225
D. Verma, N. Mishra, Analysis and prediction of breast cancer and diabetes disease datasets using data mining classification techniques, in Proceedings of the International Conference on Intelligent Sustainable Systems (ICISS), pp. 533–538 (2017)
U. Ojha, S. Goel, A study on prediction of breast cancer recurrence using data mining techniques, in 7th International Conference on Cloud Computing, Data Science & Engineering—Confluence, pp. 527–530 (2017)
B.L. Rodrigues, Analysis of the Wisconsin breast cancer dataset and machine learning for breast cancer detection, in Proceedings of XI Workshop de Visão Computational, pp. 15–19 (2015)
S. Saxena, K. Burse, A survey on neural network techniques for classification of breast cancer data. Int. J. Eng. Adv. Technol. 2(1), 234–237 (2012)
UCI Machine Learning Repository: Breast Cancer Wisconsin Dataset, https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28original%29
O.L. Mangasarian, W.H. Wolberg, Cancer diagnosis via linear programming. SIAM News 23(5), 1–18 (1990)
W.H. Wolberg, O.L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. USA. 87, 9193–9196 (1990)
O.L. Mangasarian, R. Setiono, W.H. Wolberg, Pattern recognition via linear programming: theory and application to medical diagnosis, in Large-Scale Numerical Optimization, ed. by T.F. Coleman, Y. Li (SIAM Publications, Philadelphia, 1990), pp. 22–30
K.P. Bennett, O.L. Mangasarian, Robust linear programming discrimination of two linearly inseparable sets, Optim. Method Softw. 1, 23–34 (1992). Gordon & Breach Science Publishers
V. Kumar, M. Mazzara, B.K. Mishra, D.N.H. Thanh, A. Verma, https://arxiv.org/ftp/arxiv/papers/1902/1902.03825.pdf
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Kumar, V., Mishra, B.K., Mazzara, M., Thanh, D.N.H., Verma, A. (2020). Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_43
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