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Breast Cancer Prediction with Feature-Selected XGB Classifier, Optimized by Metaheuristic Algorithms

Features refined well—
XGBoost learns cancer signs,
Early hope prevails.
Machine learning
Breast cancer
XGBoost
Feature selection
Metaheuristics

P. Sarker, A. Ksibi, M. M. Jamjoom, K. Choi, A. A. Nahid, M. A. Samad, “Breast cancer prediction with feature-selected XGB classifier, optimized by metaheuristic algorithms,” Journal of Big Data 12:78 (2025), doi: 10.1186/s40537-025-01132-7

Authors

P. Sarker

A. Ksibi

M. M. Jamjoom

K. Choi

A. A. Nahid

Md Abdus Samad

Published

January 2025

Doi

10.1186/s40537-025-01132-7

Abstract

Early and accurate breast cancer prediction is essential for improving patient outcomes. This study presents an optimized XGBoost classifier enhanced with metaheuristic algorithm-based feature selection for breast cancer prediction. The proposed approach systematically identifies the most discriminative features while optimizing model hyperparameters, achieving superior predictive performance compared to conventional machine learning methods.

Citation

 Add to Zotero 

@article{SarkerEtAl:2025b,
  Author  = {Sarker, P. and Ksibi, A. and Jamjoom, M. M. and Choi, K. and Nahid, A. A. and Samad, M. A.},
  Title   = {Breast cancer prediction with feature-selected XGB classifier, optimized by metaheuristic algorithms},
  Journal = {Journal of Big Data},
  Volume  = {12},
  Pages   = {78},
  Year    = {2025},
  Doi     = {10.1186/s40537-025-01132-7}
}