DOI: 10.7763/IJCTE.2026.V18.1402
Spearman-guided XGBoost Classification for Breast Cancer Diagnosis Using the Wisconsin Diagnostic Dataset
2. College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia
3. Cyber Security department, Faculty of Information Technology, Zarqa University, Zarqa, Jordan
4. Artificial Intelligence Department, Plekhanov Russian University of Economics in Dubai, Dubai Knowledge Park, Dubai, UAE
5. Computer Science Department, College of Computing and Intelligent Systems, University of Al Dhaid, Sharjah, UAE
6. Department of Bioelectronics, Modern University of Technology and Information (MTI) University, Egypt
7. Faculty of Engineering FEQS, INTI-IU-University, Nilai, Malaysia
8. Faculty of Management, Shinawatra University, Pathum Thani, Thailand
9. Department of Artificial Intelligence, College of Information Technology, Misr University for Science & Technology (MUST), Giza, Egypt
Email: s hattar@zu.edu.jo (H.A.); jababneh@zu.edu.jo (J.A.); Alomoush.W@reu.tech and walomoush@uodh.ac.ae (W.A.); samer.86027@eng.mti.edu.eg (S.M.S.); mohdahmed.hafez@newinti.edu.my (M.H.); Mohanad.deif@must.edu.eg (M.A.D.)
*Corresponding author
Manuscript received February 13, 2025; revised April 26, 2025; accepted December 24, 2025; published September 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
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.
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).