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<p>Breast cancer (BC) ranks among the significant causes of death rates in women globally; therefore, early and precise diagnosis is essential for efficient treatment and survival. Machine learning (ML) tools have improved BC diagnosis. This paper introduces an enhanced BC classification model that uses hybrid PSO–GWO optimization after stacking ML models. Using a dataset of BC shared online at the Kaggle repository, which consists of 4024 patients and 16 attributes, the paper applies pre-processing tasks to prepare the BC dataset. For feature selection (FS), binary versions of Particle Swarm Optimization (BPSO), Grey Wolf Optimizer (BGWO), and Whale Optimization Algorithm (BWAO) were utilized. The BPSO obtained the best fit to the dataset with 0.92025. This study employs six ML models, such as Random Forest (RF) Classifier, Stochastic Gradient Descent (SGD) Classifier, Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), k-Nearest Neighbors (k-NN), XGBoost Classifier (XGB), and Naïve Bayes Classifier (NBC) to evaluate the proposed approach. The RF achieved the best accuracy of 89.22%. The stacked models (RF, SGB, SVM, GB, and K-NN) served as base learners, with XGB as the meta-learner. The results of the stacked model showed a significant improvement in classification accuracy of 93.91%. Finally, we optimized the stacked model using a hybrid PSO–GWO algorithm to improve accuracy. The study uses the following evaluation metrics: accuracy, precision, recall, F score, and AUC. The findings show that the hybrid PSO–GWO algorithm can optimize the stacked ML model, increasing its accuracy from 93.91% to 99.49%.</p>

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Enhancing breast cancer disease classification based on stacked machine learning models with hybrid Particle Swarm and Grey Wolf Optimization algorithms

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Ahmed M. Elshewey,
  • Samah A. Z. Hassan,
  • Rasha Y. Youssef,
  • Hazem M. El-Bakry,
  • Ahmed M. Osman

摘要

Breast cancer (BC) ranks among the significant causes of death rates in women globally; therefore, early and precise diagnosis is essential for efficient treatment and survival. Machine learning (ML) tools have improved BC diagnosis. This paper introduces an enhanced BC classification model that uses hybrid PSO–GWO optimization after stacking ML models. Using a dataset of BC shared online at the Kaggle repository, which consists of 4024 patients and 16 attributes, the paper applies pre-processing tasks to prepare the BC dataset. For feature selection (FS), binary versions of Particle Swarm Optimization (BPSO), Grey Wolf Optimizer (BGWO), and Whale Optimization Algorithm (BWAO) were utilized. The BPSO obtained the best fit to the dataset with 0.92025. This study employs six ML models, such as Random Forest (RF) Classifier, Stochastic Gradient Descent (SGD) Classifier, Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), k-Nearest Neighbors (k-NN), XGBoost Classifier (XGB), and Naïve Bayes Classifier (NBC) to evaluate the proposed approach. The RF achieved the best accuracy of 89.22%. The stacked models (RF, SGB, SVM, GB, and K-NN) served as base learners, with XGB as the meta-learner. The results of the stacked model showed a significant improvement in classification accuracy of 93.91%. Finally, we optimized the stacked model using a hybrid PSO–GWO algorithm to improve accuracy. The study uses the following evaluation metrics: accuracy, precision, recall, F score, and AUC. The findings show that the hybrid PSO–GWO algorithm can optimize the stacked ML model, increasing its accuracy from 93.91% to 99.49%.