Impact of Different Kernels on Breast Cancer Severity Prediction Using Support Vector Machine
Abstract
Breast cancer poses a critical global health challenge and continues to be one of the most prevalent causes of cancer-related deaths among women worldwide. Accurate and early classification of cancer severity is essential for improving treatment outcomes and guiding clinical decision-making, since timely intervention can significantly reduce mortality rates and enhance patient survival. This study evaluates the performance of Support Vector Machine (SVM) models using different kernel functions of Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid for breast cancer severity prediction. The impact of feature selection was also examined, using the Random Forest algorithm to select the top features based on Mean Decrease Accuracy (MDA), which serves to reduce redundancy, improve interpretability, and enhance model efficiency. Experimental results show that the RBF kernel consistently outperformed other kernels, especially in terms of sensitivity, a critical metric in medical diagnostics that emphasizes the ability of the model to identify positive cases correctly. Without feature selection, the RBF kernel achieved an accuracy of 0.9744, a sensitivity of 0.9772, a precision of 0.9722, and an AUC of 0.9968, indicating strong performance across all evaluation metrics. After applying feature selection, the RBF kernel further improved the accuracy to 0.9754, the sensitivity to 0.9770, the precision to 0.9742, and the AUC to 0.9975, which demonstrated enhanced generalization and reduced overfitting, highlighting the benefits of targeted feature reduction. While the Polynomial kernel yielded the highest precision (up to 0.9799), its lower sensitivity (as low as 0.9237) indicates a greater risk of false negatives, which is particularly concerning in cancer detection. These findings underscore the importance of optimizing both kernel function and feature selection. The RBF kernel, when combined with targeted feature selection, offers the most balanced and sensitive model, making it highly suitable for breast cancer classification tasks where diagnostic accuracy is vital
Downloads
References
H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA Cancer J Clin, vol. 71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660.
S. Saadatmand, R. Bretveld, S. Siesling, and M. M. A. Tilanus-Linthorst, “Influence of tumour stage at breast cancer detection on survival in modern times: population based study in 173 797 patients,” BMJ, p. h4901, Oct. 2015, doi: 10.1136/bmj.h4901.
K. Polat and S. Güneş, “Breast cancer diagnosis using least square support vector machine,” Digit Signal Process, vol. 17, no. 4, pp. 694–701, Jul. 2007, doi: 10.1016/j.dsp.2006.10.008.
N. Cristianini and B. Scholkopf, “Support Vector Machines and Kernel Methods: The New Generation of Learning Machines,” AI Mag, vol. 23, no. 3, pp. 31–41, Sep. 2002.
R. Vinge and T. McKelvey, “Understanding Support Vector Machines with Polynomial Kernels,” in 2019 27th European Signal Processing Conference (EUSIPCO), IEEE, Sep. 2019, pp. 1–5. doi: 10.23919/EUSIPCO.2019.8903042.
S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, and W. Xu, “Applications of Support Vector Machine (SVM) Learning in Cancer Genomics,” Cancer Genomics Proteomics, vol. 15, no. 1, Jan. 2018, doi: 10.21873/cgp.20063.
F. S. Gomiasti, W. Warto, E. Kartikadarma, J. Gondohanindijo, and D. R. I. M. Setiadi, “Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine,” Journal of Computing Theories and Applications, vol. 1, no. 4, pp. 396–406, Mar. 2024, doi: 10.62411/jcta.10106.
C. S. Rao and K. Karunakara, “Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI,” Multimed Tools Appl, vol. 81, no. 5, pp. 7393–7417, Feb. 2022, doi: 10.1007/s11042-021-11821-z.
A. P. Gopi, R. N. S. Jyothi, V. L. Narayana, and K. S. Sandeep, “Classification of tweets data based on polarity using improved RBF kernel of SVM,” International Journal of Information Technology, vol. 15, no. 2, pp. 965–980, Feb. 2023, doi: 10.1007/s41870-019-00409-4.
M. Ring and B. M. Eskofier, “An approximation of the Gaussian RBF kernel for efficient classification with SVMs,” Pattern Recognit Lett, vol. 84, pp. 107–113, Dec. 2016, doi: 10.1016/j.patrec.2016.08.013.
H. Jiang, W.-K. Ching, W.-S. Cheung, W. Hou, and H. Yin, “Hadamard Kernel SVM with applications for breast cancer outcome predictions,” BMC Syst Biol, vol. 11, no. S7, p. 138, Dec. 2017, doi: 10.1186/s12918-017-0514-1.
M. N. Murty and R. Raghava, Support Vector Machines and Perceptrons. Cham: Springer International Publishing, 2016. doi: 10.1007/978-3-319-41063-0.
S. F. Hussain, “A novel robust kernel for classifying high-dimensional data using Support Vector Machines,” Expert Syst Appl, vol. 131, pp. 116–131, Oct. 2019, doi: 10.1016/j.eswa.2019.04.037.
B. Zhang, H. Shi, and H. Wang, “Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach,” J Multidiscip Healthc, vol. Volume 16, pp. 1779–1791, Jun. 2023, doi: 10.2147/JMDH.S410301.
R. R. Chandan et al., “Reviewing the Impact of Machine Learning on Disease Diagnosis and Prognosis: A Comprehensive Analysis,” Open Pain J, vol. 17, no. 1, May 2024, doi: 10.2174/0118763863291395240516093102.
Z. Zhu, Y. Sun, and B. Honarvar Shakibaei Asli, “Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools,” Electronics (Basel), vol. 13, no. 17, p. 3575, Sep. 2024, doi: 10.3390/electronics13173575.
M. R. Darbandi, M. Darbandi, S. Darbandi, I. Bado, M. Hadizadeh, and H. R. Khorram Khorshid, “Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: A multimethod study,” Eur J Cancer, vol. 209, p. 114227, Sep. 2024, doi: 10.1016/j.ejca.2024.114227.
A. Soliman, Z. Li, and A. V. Parwani, “Artificial intelligence’s impact on breast cancer pathology: a literature review,” Diagn Pathol, vol. 19, no. 1, p. 38, Feb. 2024, doi: 10.1186/s13000-024-01453-w.
A. Singh, A. Singh, and S. Bhattacharya, “Research trends on AI in breast cancer diagnosis, and treatment over two decades,” Discover Oncology, vol. 15, no. 1, p. 772, Dec. 2024, doi: 10.1007/s12672-024-01671-0.
T. Fujioka et al., “The Future of Breast Cancer Diagnosis in Japan with AI and Ultrasonography,” JMA J, vol. 8, no. 1, pp. 91–101, 2024, doi: 10.31662/jmaj.2024-0183.
B. Ghaddar and J. Naoum-Sawaya, “High dimensional data classification and feature selection using support vector machines,” Eur J Oper Res, vol. 265, no. 3, pp. 993–1004, Mar. 2018, doi: 10.1016/j.ejor.2017.08.040.
W. Zeng, J. Jia, Z. Zheng, C. Xie, and L. Guo, “A comparison study: Support vector machines for binary classification in machine learning,” in 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), IEEE, Oct. 2011, pp. 1621–1625. doi: 10.1109/BMEI.2011.6098517.
B. S. Abunasser, M. R. J. AL-Hiealy, I. S. Zaqout, and S. S. Abu-Naser, “Breast Cancer Detection and Classification using Deep Learning Xception Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, 2022, doi: 10.14569/IJACSA.2022.0130729.
H. Jiang and W.-K. Ching, “Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data,” Comput Math Methods Med, vol. 2012, pp. 1–7, 2012, doi: 10.1155/2012/205025.
S. A. Korkmaz and M. Poyraz, “Least Square Support Vector Machine and Minumum Redundacy Maximum Relavance for Diagnosis of Breast Cancer from Breast Microscopic Images,” Procedia Soc Behav Sci, vol. 174, pp. 4026–4031, Feb. 2015, doi: 10.1016/j.sbspro.2015.01.1150.
D. A. Ragab, M. Sharkas, S. Marshall, and J. Ren, “Breast cancer detection using deep convolutional neural networks and support vector machines,” PeerJ, vol. 7, p. e6201, Jan. 2019, doi: 10.7717/peerj.6201.
M. P. Behera, A. Sarangi, D. Mishra, and S. K. Sarangi, “A Hybrid Machine Learning algorithm for Heart and Liver Disease Prediction Using Modified Particle Swarm Optimization with Support Vector Machine,” Procedia Comput Sci, vol. 218, pp. 818–827, 2023, doi: 10.1016/j.procs.2023.01.062.
A. Patle and D. S. Chouhan, “SVM kernel functions for classification,” in 2013 International Conference on Advances in Technology and Engineering (ICATE), IEEE, Jan. 2013, pp. 1–9. doi: 10.1109/ICAdTE.2013.6524743.
T. Kavzoglu and I. Colkesen, “A kernel functions analysis for support vector machines for land cover classification,” International Journal of Applied Earth Observation and Geoinformation, vol. 11, no. 5, pp. 352–359, Oct. 2009, doi: 10.1016/j.jag.2009.06.002.
R. Fernandes de Mello and M. Antonelli Ponti, “Statistical Learning Theory,” in Machine Learning, Cham: Springer International Publishing, 2018, pp. 75–128. doi: 10.1007/978-3-319-94989-5_2.
M. Azzeh, Y. Elsheikh, A. B. Nassif, and L. Angelis, “Examining the performance of kernel methods for software defect prediction based on support vector machine,” Sci Comput Program, vol. 226, p. 102916, Mar. 2023, doi: 10.1016/j.scico.2022.102916.
A. Goel and S. Kr. Srivastava, “Role of Kernel Parameters in Performance Evaluation of SVM,” in 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), IEEE, Feb. 2016, pp. 166–169. doi: 10.1109/CICT.2016.40.
P. El Kafrawy, H. Fathi, M. Qaraad, A. K. Kelany, and X. Chen, “An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data,” IEEE Access, vol. 9, pp. 155353–155369, 2021, doi: 10.1109/ACCESS.2021.3123090.
T. Ebina, H. Toh, and Y. Kuroda, “DROP: an SVM domain linker predictor trained with optimal features selected by random forest,” Bioinformatics, vol. 27, no. 4, pp. 487–494, Feb. 2011, doi: 10.1093/bioinformatics/btq700.
B. Kalpana et al., “Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification,” Computer Systems Science and Engineering, vol. 44, no. 3, pp. 2243–2257, 2023, doi: 10.32604/csse.2023.027129.
R. Genuer, J.-M. Poggi, and C. Tuleau-Malot, “Variable selection using random forests,” Pattern Recognit Lett, vol. 31, no. 14, pp. 2225–2236, Oct. 2010, doi: 10.1016/j.patrec.2010.03.014.
A.-L. Boulesteix and M. Slawski, “Stability and aggregation of ranked gene lists,” Brief Bioinform, vol. 10, no. 5, pp. 556–568, Sep. 2009, doi: 10.1093/bib/bbp034.
C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional variable importance for random forests,” BMC Bioinformatics, vol. 9, no. 1, p. 307, Dec. 2008, doi: 10.1186/1471-2105-9-307.
G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan, “Learning the Kernel Matrix with Semidefinite Programming,” Journal of Machine Learning Research, vol. 5, pp. 27–72, 2004.
H.-T. Lin and Chih-Jen Lin., “A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.,” Neural Comput, vol. 3, no. 16, pp. 1–32, 2003.
A. Bahl et al., “Recursive feature elimination in random forest classification supports nanomaterial grouping,” NanoImpact, vol. 15, p. 100179, Mar. 2019, doi: 10.1016/j.impact.2019.100179.
R. S.V.G, R. K.Thammi, K. V. Valli, and V. Kamadi VSRP, “An SVM Based Approach to Breast Cancer Classification using RBF and Polynomial Kernel Functions with Varying Arguments,” International Journal of Computer Science and Information Technologies, vol. 5, no. 4, pp. 5901–5904, 2014.
G. A. Pethunachiyar, “Classification Of Diabetes Patients Using Kernel Based Support Vector Machines,” in 2020 International Conference on Computer Communication and Informatics (ICCCI), IEEE, Jan. 2020, pp. 1–4. doi: 10.1109/ICCCI48352.2020.9104185.
Zhen-Dong Zhao, Y.-Y. Lou, Jun-Hong Ni, and Jing Zhang, “RBF-SVM and its application on reliability evaluation of electric power system communication network,” in 2009 International Conference on Machine Learning and Cybernetics, IEEE, Jul. 2009, pp. 1188–1193. doi: 10.1109/ICMLC.2009.5212365.
X. Ding, J. Liu, F. Yang, and J. Cao, “Random radial basis function kernel-based support vector machine,” J Franklin Inst, vol. 358, no. 18, pp. 10121–10140, Dec. 2021, doi: 10.1016/j.jfranklin.2021.10.005.
Copyright (c) 2026 Kunti Mahmudah, Sugiyarto Surono, Rusmining Rusmining, Fatma Indriani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.png)
.png)
.png)
.png)
.png)
