doi: 10.7763/IJCTE.2026.V18.1402
Spearman-guided XGBoost Classification for Breast Cancer Diagnosis Using the Wisconsin Diagnostic Dataset
Hani Attar1,2,*
,
Jafar Ababneh3 ,
Waleed Alomoush4,5 ,
Samer M. Sharfo6 ,
Mohamad Hafez7,8 ,
Mohanad A. Deif9
- 1Faculty of Engineering, Zarqa University, Zarqa, Jordan
- 2College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia
- 3Cyber Security department, Faculty of Information Technology, Zarqa University, Zarqa, Jordan
- 4Artificial Intelligence Department, Plekhanov Russian University of Economics in Dubai, Dubai Knowledge Park, Dubai, UAE
- 5Computer Science Department, College of Computing and Intelligent Systems, University of Al Dhaid, Sharjah, UAE
- 6Department of Bioelectronics, Modern University of Technology and Information (MTI) University, Egypt
- 7Faculty of Engineering FEQS, INTI-IU-University, Nilai, Malaysia
- 8Faculty of Management, Shinawatra University, Pathum Thani, Thailand
- 9Department of Artificial Intelligence, College of Information Technology, Misr University for Science & Technology (MUST), Giza, Egypt
- Manuscript receivedFebruary 13, 2025
- revisedApril 26, 2025
- acceptedDecember 24, 2025
- publishedSeptember 15, 2026
Abstract
Breast cancer remains one of the leading causes of mortality among women worldwide, underscoring the critical need for effective and early diagnostic tools. This study presents a comprehensive Machine Learning (ML) framework that employs k-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) algorithms to analyze a breast cancer dataset obtained from the University of California, Irvine (UCI) repository. The dataset was partitioned into 70% training and 30% testing subsets to evaluate the generalization performance of each model. The Logistic Regression (LR) model achieved the highest accuracy at 96.5%, demonstrating its effectiveness in modeling linear decision boundaries. The RF algorithm followed with 95.3% accuracy, reflecting its capability in capturing complex, non-linear interactions. XGBoost achieved a competitive accuracy of 95.9%, highlighting its robustness in detecting subtle patterns and improving predictive performance. The KNN model recorded an accuracy of 92.9%, indicating its effectiveness in identifying localized patterns within the data. These findings underscore the potential of ML-driven approaches in enhancing breast cancer diagnostics and contribute meaningfully to public health by supporting timely and accurate disease detection.
Keywords
- Machine Learning (ML)
- breast cancer classification
- linear relationships
- classification algorithms
- cancer prediction
- supervised learning
How to Cite
Hani Attar, Jafar Ababneh, Waleed Alomoush, Samer M. Sharfo, Mohamad Hafez, and Mohanad A. Deif, "Spearman-guided XGBoost Classification for Breast Cancer Diagnosis Using the Wisconsin Diagnostic Dataset," International Journal of Computer Theory and Engineering, vol. 18, no. 3, pp. 218-225, 2026. https://doi.org/10.7763/IJCTE.2026.V18.1402
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).