Citation

P., Ashokkumar and TV, Sateesh Kumar and Khan, Mudassir and Mohd Su'ud, Mazliham and Alam, Muhammad Mansoor and Mallik, Saurav (2025) Ant Colony Optimization for feature selection in breast cancer classification. Egyptian Informatics Journal, 32. ISSN 1110-8665

Text 1-s2.0-S1110866525002403-main.pdf - Published Version Restricted to Repository staff only Download (4MB)

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Ant colony optimization, breast cancer classification, feature selection, machine learning models
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	09 Dec 2025 03:31
Last Modified:	12 Dec 2025 23:50
URII:	http://shdl.mmu.edu.my/id/eprint/14976

Downloads

Edit (login required)