References
- L. Yang and Z. Xu, "Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning", International Journal of Machine Learning and Cybernetics, Vol. 10, No. 3, pp. 591-601, 2019. https://doi.org/10.1007/s13042-017-0741-1
- R. Vijayarajeswari, P. Parthasarathy, S. Vivekanandan, "Classification of mammogram for early detection of breast cancer using SVM classifier and Houghtransform", Measurement, Vol. 146, pp. 800-805, 2019. https://doi.org/10.1016/j.measurement.2019.05.083
- P. Filipczuk, M. Kowal, "Automatic breast cancer diagnosis based on k-means Clustering and adaptive thresholding hybrid segmentation", Image processing and communications challenges, Springer, pp. 295-302, 2011.
- Youness Khourdifi, Mohamed Bahaj,"Applying Best Machine Learning Algorithms for Breast Cancer Prediction and Classification", International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), 2018.
- A. K. Dubey, U. Gupta, and S. Jain,"Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data", International Journal on Advanced Science, Engineering and Information Technology, Vol.8, No. 1, pp. 18-29, 2018. https://doi.org/10.18517/ijaseit.8.1.3490
- F. AlFayez, M. W. A. El-Soud, and T. Gaber, "Thermogram Breast Cancer Detection: a comparative study of two machine learning techniques", Applied Sciences, Vol. 10, No. 551, pp. 1-20, 2020.
- D. A. Omondiagbe, S. Veeramani, and A. S. Sidhu, "Machine Learning Classification Techniques for Breast Cancer Diagnosis", In: Proc. of IOP Conf. Series: Materials Science and Engineering, Vol. 495, pp. 1-16, 2019.
- L. Tapak, N. Shirmohammadi-Khorram, P. Amini, B. Alafchi, O. Hamidi, and J. Poorolajal, "Prediction of survival and metastasis in breast cancer patients using machine learning classifiers", Clinical Epidemiology and Global Health, Vol. 7, No. 3, pp. 293-299, 2019. https://doi.org/10.1016/j.cegh.2018.10.003
- Y. J. Tseng, C. E. Huang, C. N. Wen, P. Y. Lai, M. H. Wu, Y. C. Sun, H. Y. Wang, and J. J. Lu, "Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies", International Journal of Medical Informatics, Vol. 128, pp. 79-86, 2019. https://doi.org/10.1016/j.ijmedinf.2019.05.003
- Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms Bichen Zheng, Sang Won Yoon , Sarah S. Lam -Expert Systems with Applications ,Volume 41, Issue 4, Part 1, March 2014, Pages 1476-1482. https://doi.org/10.1016/j.eswa.2013.08.044
- Bennett, K. P., & Blue, J. A. (1998). A support vector machine approach to decision trees. In Proceedings of IEEE world congress on computational intelligence (pp. 2396-2401). Anchorage, AK: IEE.
- Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36, 3240-3247. https://doi.org/10.1016/j.eswa.2008.01.009
- F. Liu and M. Brown, "Breast Cancer Recognition by Support Vector Machine Combined with Daubechies Wavelet Transform and Principal Component Analysis", In: Proc. of the International Conf. on ISMAC in Computational Vision and Bio-Engineering, Springer, pp. 1921-1930, 2018.
- P. Exarchos a, Michalis V. Karamouzis "Machine learning applications in cancer prognosis and prediction" Computational and Structural Biotechnology Journal 13 (2015) 8-17. https://doi.org/10.1016/j.csbj.2014.11.005
- L. Yang and Z. Xu, "Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning", International Journal of Machine Learning and Cybernetics, Vol. 10, No. 3, pp. 591-601, 2019. https://doi.org/10.1007/s13042-017-0741-1
- V. Chaurasia, S. Pal, and B. Tiwari, "Prediction of benign and malignant breast cancer using data mining techniques", Journal of Algorithms and Computational Technology, Vol. 12, No. 2, pp.119-126, 2018. https://doi.org/10.1177/1748301818756225
- H. M. Moftah, A. T. Azar, E. T. Al-Shammari, N. I., "Adaptive k-means Clustering algorithm for MR breast image segmentation", Neural Computing and Applications, Vol. 24, No. 7-8, pp. 1917-1928, 2014. https://doi.org/10.1007/s00521-013-1437-4
- A. K. Dubey, U. Gupta, and S. Jain, "Analysis of k-means Clustering approach on the breast cancer Wisconsin dataset", International Journal of Computer Assisted Radiology and Surgery, Vol. 11, No. 11, pp. 2033-2047, 2016. https://doi.org/10.1007/s11548-016-1437-9
- W. L. Al-Yaseen, Z. A. Othman. "Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system", Expert Systems with Applications, Vol.67, pp. 296-303, 2017. https://doi.org/10.1016/j.eswa.2016.09.041
- M. Kumar, A. J. Kulkarni, and S. C. Satapathy, "A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure Using Fuzzy C-means and Cohort Intelligence", Optimization in Machine Learning and Applications, Springer, pp. 113-126, 2020.
- G. F. Stark, G. R. Hart, B. J. Nartowt, and J. Deng, "Predicting breast cancer risk using personal health data and machine learning models", Plos One, Vol. 14, No. 12, pp. 1-17, 2019.
- P. Ferroni, F. M. Zanzotto, S. Riondino, N. Scarpato, F. Guadagni, and M. Roselli, "Breast cancer prognosis using a machine learning approach", Cancers, Vol. 11, No. 328, pp. 1-9, 2019.
- C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification", Computer Science, pp. 1-16, 2008.
- Abien Fred M. Agarap,"On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset", ICMLSC , February 2-4, 2018, Phu Quoc Island, Viet Nam, 2018.
- Dana Bazazeh and Raed Shubair ,"Comparative Study of Machine Learning Algorithms for Breast Cancer Detection and Diagnosis", 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), 2016.
- Hui-Ling Chen , Bo Yang, Jie Liu, Da-You Liu,"A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis,H.-L. Chen et al. - Expert Systems with Applications 38 9014-9022, 2015 https://doi.org/10.1016/j.eswa.2011.01.120
- Muhammad Hussain, Summrina Kanwal Wajid, Ali Elzaar, Mohammed Berbar,"A Comparison of SVM Kernel Functions for Breast Cancer Detection", Eighth International Conference Computer Graphics, Imaging and Visualization, 2014.