Breast Cancer Diagnosis: Feature Selection and Ensemble Machine Learning

Title

Breast Cancer Diagnosis: Feature Selection and Ensemble Machine Learning

Creator

Dharna, Khushi; Singh, Shivangi; Jose, Deepa V.

Description

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.

Source

Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1887-1894

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Breast Cancer; Ensemble Learning; Feature Selection; LightGBM; Machine Learning; SMOTE

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159929-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICECA66444.2025.11383128

https://www.scopus.com/pages/publications/105034842534?origin=resultslist