Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance
Main Article Content
Abstract
This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
Downloads
Article Details
Copyright (c) 2024 Mohammad Diqi, Marselina Endah Hiswati, Hamzah Hamzah, I Wayan Ordiyasa, Sri Hasta Mulyani, Nurhadi Wijaya, Putra Wanda
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
J. Li et al., “Non-Invasive Biomarkers for Early Detection of Breast Cancer,” Cancers, vol. 12, no. 10, p. 2767, 2020, doi: 10.3390/cancers12102767.
M. S. A. El-Eneen, H. A. Seif, R. K. Fawzy, and A. M. Mossa, “Determination of Thymidine Kinase 1 (TK1) Level as a Risk Warning Biomarker to Improve Early Detection of Breast Cancer,” Cancer Research Journal, vol. 7, no. 4, p. 161, 2019, doi: 10.11648/j.crj.20190704.17.
L. Chen et al., “Local Extraction and Detection of Early Stage Breast Cancers Through a Microneedle and Nano-Ag/MBL Film Based Painless and Blood-Free Strategy,” Materials Science and Engineering C, vol. 109, p. 110402, 2020, doi: 10.1016/j.msec.2019.110402.
T. Crook et al., “Accurate Screening for Early-Stage Breast Cancer by Detection and Profiling of Circulating Tumor Cells,” Cancers, vol. 14, no. 14, p. 3341, 2022, doi: 10.3390/cancers14143341.
I. García-Murillas et al., “Assessment of Molecular Relapse Detection in Early-Stage Breast Cancer,” Jama Oncology, vol. 5, no. 10, p. 1473, 2019, doi: 10.1001/jamaoncol.2019.1838.
A. Balyan, Y. Singh, and Shashank, “Classifying Breast Cancer Based on Machine Learning,” in Proceedings of International Conference on Artificial Intelligence and Applications, P. Bansal, M. Tushir, V. E. Balas, and R. Srivastava, Eds., Singapore: Springer Singapore, 2021, pp. 35–44.
J. Sunny, N. D. Rane, R. Kanade, and S. Devi, “Breast Cancer Classification and Prediction Using Machine Learning,” International Journal of Engineering Research And, vol. V9, no. 02, 2020, doi: 10.17577/ijertv9is020280.
J. Wu and C. Hicks, “Breast Cancer Type Classification Using Machine Learning,” Journal of Personalized Medicine, vol. 11, no. 2, p. 61, 2021, doi: 10.3390/jpm11020061.
L. Alkhathlan and A. K. J. Saudagar, “Predicting and Classifying Breast Cancer Using Machine Learning,” Journal of Computational Biology, vol. 29, no. 6, pp. 497–514, 2022, doi: 10.1089/cmb.2021.0236.
E. Michael, H. Ma, H. Li, and S. Qi, “An Optimized Framework for Breast Cancer Classification Using Machine Learning,” Biomed Research International, vol. 2022, pp. 1–18, 2022, doi: 10.1155/2022/8482022.
A. Gupta, L. Kumar, R. Jain, and P. Nagrath, “Heart Disease Prediction Using Classification (Naive Bayes),” pp. 561–573, 2020, doi: 10.1007/978-981-15-3369-3_42.
T. S. Kumar and P. Jagadeesh, “Detection and Classification of Tumor Cells From Bone X-Ray Imagery Using SVM Classifier With Naïve Bayes Classifier,” 2023, doi: 10.1109/accai58221.2023.10199612.
G. Liang, Y. Yan, M. Wang, X. Lian, M. S. Li, and W. Tang, “Classification for Text Data From the Power System Based on Improving Naive Bayes,” 2020, doi: 10.1109/appeec48164.2020.9220634.
W. N. L. W. H. Ibeni, M. Z. M. Salikon, S. A. Daud, and M. N. M. Salleh, “Comparative Analysis on Bayesian Classification for Breast Cancer Problem,” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 4, 2019, doi: 10.11591/eei.v8i4.1628.
P. S. Nishant, S. Mehrotra, B. G. K. Mohan, and G. Devaraju, “Identifying Classification Technique for Medical Diagnosis,” pp. 95–104, 2020, doi: 10.1007/978-981-15-0630-7_10.
F. Sun and X. Xie, “Deep Non-Parallel Hyperplane Support Vector Machine for Classification,” Ieee Access, vol. 11, pp. 7759–7767, 2023, doi: 10.1109/access.2023.3237641.
O. Okwuashi and C. E. Ndehedehe, “Deep Support Vector Machine for Hyperspectral Image Classification,” Pattern Recognition, vol. 103, p. 107298, 2020, doi: 10.1016/j.patcog.2020.107298.
L. Jiang, L. Zhang, L. Yu, and D. Wang, “Class-Specific Attribute Weighted Naive Bayes,” Pattern Recognition, vol. 88, pp. 321–330, 2019, doi: 10.1016/j.patcog.2018.11.032.
D. Chicco, N. Tötsch, and G. Jurman, “The Matthews Correlation Coefficient (MCC) Is More Reliable Than Balanced Accuracy, Bookmaker Informedness, and Markedness in Two-Class Confusion Matrix Evaluation,” Biodata Mining, vol. 14, no. 1, 2021, doi: 10.1186/s13040-021-00244-z.
M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” Ieee Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/access.2022.3151048.