Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Dharna, Khushi; Singh, Shivangi; Jose, Deepa V. Title A name given to the resource Breast Cancer Diagnosis: Feature Selection and Ensemble Machine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1887-1894 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECA66444.2025.11383128" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECA66444.2025.11383128</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034842534?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034842534?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Breast Cancer; Ensemble Learning; Feature Selection; LightGBM; Machine Learning; SMOTE Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159929-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online