Breast cancer classification using machine learning techniques: a comparative study
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Abstract
Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare.
Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification. The used dataset is Wisconsin diagnosis Breast Cancer.
Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification.
Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are performant ( good accuracy specificity and sensitivity) compared to the other evaluated algorithms.
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U.S. Cancer Statistics Working Group. United States Cancer Statistics: 19992008 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute (2012).
BF Cruz, JT de Assis, VV Estrela, A Khelassi, A compact SIFT-based strategy for visual information retrieval in large image databases, Medical Technologies Journal 3 (2), 402-412, 2019 https://doi.org/10.26415/2572-004X-vol3iss2p402-412
Q Memon, (2019), On assisted living of paralyzed persons through real-time eye features tracking and classification using Support Vector Machines, Medical Technologies Journal 3 (1), 316-333 https://doi.org/10.26415/2572-004X-vol3iss1p316-333
Devi I., Karpagam G.R. and Vinoth Kumar B (2017), A survey of machine learning techniques. International Journal of Computational Systems Engineering. 3 (4): 203-212. https://doi.org/10.1504/IJCSYSE.2017.10010099
Abdel-Zaher Ahmed M. and Eldeib Ayman M. (2016), Breast cancer classification using deep belief networks. Expert Systems with Applications. ELSEVIER;46:139-144. https://doi.org/10.1016/j.eswa.2015.10.015
Thein HTT. and Khin MMT. (2015), An Approach for Breast Cancer Diagnosis Classification Using Neural Network. Advanced Computing. An International Journal (ACIJ). 6 (1): 1-11. https://doi.org/10.5121/acij.2015.6101
Ashraf O. I. and Siti, M. S. (2018), Intelligent breast cancer diagnosis based on enhanced Pareto optimal and multilayer perceptron neural network. International Journal of Computer Aided Engineering and Technology. Inderscience. 10 (5): 543-556. https://doi.org/10.1504/IJCAET.2018.10013710
Guan J., Lin L., Ji G., Lin C., Le T., Imre JR. (2016), Breast Tumor Computer-aided Diagnosis using Self-Validating Cerebellar Model Neural Networks. Acta Polytechnica Hungarica. 13 (4): 39-52. https://doi.org/10.12700/APH.13.4.2016.4.3
Karthik Kumar U., Sai Nikhil M.B. and Sumangali K. (2017), Prediction of Breast Cancer using Voting Classifier Technique. IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM); 2017 2 - 4 August; Veltech Dr.RR & Dr.SR University, Chennai, T.N., India. 108-114 https://doi.org/10.1109/ICSTM.2017.8089135
Mittal D., Gaurav D. and Sanjiban SR. (2015), An Effective Hybridized Classifier for Breast Cancer Diagnosis. IEEE International Conference on Advanced Intelligent Mechatronics (AIM); 2015 July 7-11. Busan, Korea. https://doi.org/10.1109/AIM.2015.7222674
Haifeng W., Bichen Z., Sang W.Y., Hoo S. K. (2017), A Support Vector Machine-Based Ensemble Algorithm for Breast Cancer Diagnosis. European Journal of Operational Research. Elsevier: 1-33.
Emina A., Abdulhamit S. (2015), Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput & Applic. Springer.
Zheng B., Sang WY., Sarah SL. (2013), Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications. Elsevier: 1-7.
Arpit, B., Aruna, T. (2015), Breast Cancer Diagnosis Using Genetically Optimized Neural Network Model. Expert Systems with Applications. Elsevier: 1-15.
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic), last accessed September 20, 2019.
E. Kreyszig (1979), Advanced Engineering Mathematics (Fourth ed.). Wiley, ISBN 0-471-02140-7.
Aurélien Géron (2017), Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Published by O'Reilly Media.
Amit K. and Bikash KS. (2017), A case study on machine learning and classification. International Journal Information and Decision Sciences 9 (2): 97-208 https://doi.org/10.1504/IJIDS.2017.084885
Francois Chollet (2018), Deep Learning with Python. Published by Manning Publications.
Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning - ICML '06.
https://doi.org/10.1145/1143844.1143874
Piri, S., Delen, D., & Liu, T. (2018). A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets. Decision Support Systems, 106, 15-29. https://doi.org/10.1016/j.dss.2017.11.006
Razmjooy N., Estrela VV., Loschi HJ. (2019), A study on metaheuristic-based neural networks for image segmentation purposes, Data Science Theory, Analysis and Applications, Taylor and Francis, Abingdon, UK, 2019. https://doi.org/10.1201/9780429263798-2
Razmjooy N., N, Estrela V.V., Loschi H.J., Farfan W.S. (2019), A Comprehensive Survey of New Metaheuristic Algorithms, Wiley.
Karim CN., Mohamed O, Ryad T. (2018), A new approach for breast abnormality detection based on thermography. Medical Technologies Journal, 2(3):245-254.
https://doi.org/10.26415/2572-004X-vol2iss3p245-254
Hemanth J., Estrela V.V. (2017), Deep Learning for Image Processing Applications. Advances in Parallel Computing, Vol. 31, IOS Press, Amsterdam, Netherlands. ISSN: 978-1-61499-822-8. https://www.iospress.nl/book/deep-learning-for-imageprocessing-applications/
Souadih K., Belaid A, Ben Salem D. (2019), Automatic Segmentation of the Sphenoid Sinus in CT-Scans Volume with Deep Medics 3D CNN Architecture, Medical Technologies Journal, Vol. 3, no. 1, pp. 334-46, https://doi.org/10.26415/2572-004X-vol3iss1p334-346