Hamza Khan, Kiran Vokkarne and Raji Sundararajan*

School of Engineering Technology, Purdue University, West Lafayette, IN-47907, USA

*Corresponding Author: Raji Sundararajan, School of Engineering Technology, Purdue University, West Lafayette, IN-47907, USA.

Received: March 16, 2026; Published: May 06, 2026

With a woman dying every 50 seconds, breast cancer is still a leading cancer of women worldwide, despite all the advanced, millions of dollars research. With early and more accurate diagnosis using efficient supervised machine learning models, it is possible to improve the prognosis of breast cancer patients. Towards this, in this study, six supervised machine learning (ML) models-Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were studied using the Wisconsin Breast Cancer Diagnostic dataset. The dataset contains records of 569 patients, 357 benign and 212 malignant, each with thirty quantitative features extracted from digitized fine-needle aspiration cytology images. Models were trained on 80% of the data and evaluated on the remaining 20%. Among all classifiers, the SVM with the radial basis function kernel showed the highest accuracy of 98.25%. This could be because this method finds the clearest possible separation between malignant and benign tumors and can curve that separation when the data is not linearly separable. These findings highlight the strong potential of ML-based systems as diagnostic decision-support tools for better breast cancer detection.

Keywords:Breast Cancer; Machine Learning; Wisconsin Dataset; SVM

References

1. Bray F., et al. “Global cancer statistics and trends”. CA: A Cancer Journal for Clinicians (2023).
2. Topol E. “High-performance medicine: the convergence of AI and healthcare”. Nature Medicine (2019).
3. Ashraf A., et al. “A review of breast cancer classification using FNA cytology and machine learning”. Diagnostics (2020).
4. Gupta D and Bhavsar H. “SVM-based cancer diagnosis: a comprehensive review”. Computer Methods and Programs in Biomedicine.
5. , et al. “Using three machine learning techniques for predicting breast cancer recurrence”. Journal of Health and Medical Informatics 4 (2013): 2.
6. Uğuz H. “A two-stage feature selection method for breast cancer diagnosis using machine learning”. Computers in Biology and Medicine (2019).
7. , et al. “Extraction of key features and enhanced prediction framework of breast cancer occurrence”. Proceedings of the Sixth International Conference on Trends in Electronics and Informatics (ICOEI 2022). IEEE (2022).
8. , et al. “Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI”. Scientific Reports 14 (2024): 8487.
9. , et al. “Early detection of Alzheimer’s disease with blood plasma proteins using support vector machines”. IEEE Journal of Biomedical and Health Informatics 25 (2020): 218-226.
10. , et al. “A machine leaning approach for the differential diagnosis of Alzheimer and vascular demetia fed by MRI selected features”. Frontiers in Neuroinformation 14 (2020): 25.
11. Berrar D. “Performance measures for binary classification in medical decision making”. Artificial Intelligence in Medicine (2021).
12. Google Research. Google Colaboratory (2024).
13. Raschka S and Mirjalili V. “Python Machine Learning”. 3^rd Packt Publishing (2019).
14. Berrar D. “Cross-validation and train test split in medical machine learning”. Encyclopedia of Bioinformatics and Computational Biology (2019).
15. Géron A. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”. 3^rd O’Reilly Media (2022).
16. Agarap AF. “On breast cancer detection using machine learning”. Proceedings of the 2017 International Conference on Machine Learning Applications (ICMLA), IEEE (2017): 1–6.
17. Jeyasingh M. “Breast cancer prediction using supervised machine learning techniques”. International Journal of Engineering Research and Technology 5 (2017): 45–50.
18. Entezari M and Hassanpour H. “Breast cancer diagnosis using machine learning algorithms”. Journal of Medical Systems6 (2018): Article 112.
19. Patgiri R and Ahmed A. “Machine learning approaches for breast cancer detection”. International Journal of Advanced Computer Science and Applications3 (2019): 95–102.
20. Chicco D and Jurman G. “The advantages of the Matthews correlation coefficient a over F1 score and accuracy”. BMC Genomics (2020).

Citation

Citation: Raji Sundararajan., et al. “Supervised Machine Learning Models for Wisconsin Breast Cancer Diagnostic Dataset Study". Acta Scientific Medical Sciences 10.5 (2026): 20-30.

Copyright

Copyright: © 2026 Raji Sundararajan., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.