BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

Shafaq Abbas; Zunera Jalil; Abdul Rehman Javed; Iqra Batool; Mohammad Zubair Khan; Abdulfattah Noorwali; Thippa Reddy Gadekallu; Aqsa Akbar

doi:10.7717/peerj-cs.390

BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

PeerJ Comput Sci. 2021 Mar 12:7:e390. doi: 10.7717/peerj-cs.390. eCollection 2021.

Authors

Shafaq Abbas¹, Zunera Jalil², Abdul Rehman Javed², Iqra Batool¹, Mohammad Zubair Khan³, Abdulfattah Noorwali⁴, Thippa Reddy Gadekallu⁵, Aqsa Akbar¹

Affiliations

¹ Department of Computer Science, Air University, Islamabad, Pakistan.
² Department of Cyber Security, Air University, Islamabad, Pakistan.
³ Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia.
⁴ Electrical Engineering Department, Umm Al-Qura University, Makkah, Saudi Arabia.
⁵ School of Information Technology and Engineering, Vellore Institute of Technology University, Tamil Nadu, India.

Abstract

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

Keywords: Breast cancer; Machine learning; Support vector machine; Whale optimization algorithm.

Grants and funding

This work was supported by the deanship of scientific research at Umm Al-Qura University “19-ENG-1-01-0015”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.