Breast Cancer Diagnosis: Feature Selection and Ensemble Machine Learning
Breast Cancer; Ensemble Learning; Feature Selection; LightGBM; Machine Learning; SMOTE
Breast cancer diagnosis requires accurate diagnostic tools that are both efficient and interpretable for clinical deployment. This study presents an integrated pipeline combining Recursive Feature Elimination with Cross-Validation (RFECV), Synthetic Minority Over-sampling Technique (SMOTE), and ensemble learning methods applied to the Wisconsin Breast Cancer Diagnostic dataset. RFECV achieved dimensionality reduction from 30 to 17 features, representing a 43% reduction while maintaining predictive performance. SMOTE transformed the class imbalance ratio from 1.68:1 to a perfect 1:1 balance. A comprehensive evaluation of twelve machine learning models revealed that LightGBM attained an F1-score of 0.9722, accuracy of 96.5%, and ROC-AUC of 0.9914 with strong cross-validation stability (0.9681 0.0179). Feature importance analysis identified worst perimeter, area, and concave points as the most discriminative features for differentiating malignant from benign tumors. The proposed approach achieved a 35% reduction in training time compared to full-featured models without sacrificing performance. This reproducible pipeline demonstrates practical clinical relevance for automated breast cancer diagnosis with improved computational efficiency and model interpretability. 2025 IEEE.
Dharna, Khushi; Singh, Shivangi; Jose, Deepa V.
Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1887-1894
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICECA66444.2025.11383128 https://www.scopus.com/pages/publications/105034842534?origin=resultslist
Dharna K., Christ University, Department of Computer Science, Bengaluru, India; Singh S., Christ University, Department of Computer Science, Bengaluru, India; Jose D.V., Christ University, Department of Computer Science, Bengaluru, India