Ant Colony Optimization for feature selection in breast cancer classification

Breast cancer remains one of the world’s most prevalent cancers that mostly affects women. Recent advancements in machine learning enable early detection of breast cancer with high accuracy, significantly reducing the mortality risk. Feature selection plays a crucial role in enhancing the performance of machine learning classification models by reducing dimensions, optimizing classification outcomes, and improving computational efficiency. In this study, we propose a nature-inspired feature selection method using Ant Colony Optimization (ACO) to enhance the classification of breast cancer. We have utilized soft voting of five machine learning models: k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) to evaluate the proposed methods. The experimental results on the two benchmark datasets, Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) demonstrates the effectiveness of the proposed approach, achieving classification accuracies of 99.79 and 99.71, respectively, outperforming the existing state-of-the-art methods.