Title: Past, present and future of gene feature selection for breast cancer classification – a survey

Authors: Chiranji Lal Chowdhary; Neelu Khare; Harshita Patel; Srinivas Koppu; Rajesh Kaluri; Dharmendra Singh Rajput

Addresses: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore TN, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore TN, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore TN, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore TN, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore TN, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore TN, 632014, India

Abstract: Computational-based analysis of gene expression to evaluate the genetic pattern provides better breast cancer prediction. It is a challenge to identify these samples correctly and effectively. Overcoming the curse of dimensionality is another challenge in feature selection. It has gained a lot of interest in the classification of cancer-based on a molecular level as it offers a systematic, precise and reliable diagnosis for different types of cancer. Machine learning (ML) algorithms are applied in a wide range of applications such as drug discovery, prediction of cancer and diagnosis. This survey paper focused on the critical steps of computer-aided detection systems: image acquisition procedures, techniques, feature extraction, and classification methods used in 2010-2020 in the field of gene expression-based cancer diagnosis. Finally, this paper ends with concluding notes and future directions. This survey is intended to be a guide for the real-time use of recent advances in gene expression-based cancer diagnosis.

Keywords: breast cancer; classification; gene selection; k-nearest neighbour; KNN; microarray analysis; support vector machine; SVM.

DOI: 10.1504/IJESMS.2022.123355

International Journal of Engineering Systems Modelling and Simulation, 2022 Vol.13 No.2, pp.140 - 153

Received: 15 Feb 2021
Accepted: 13 Jul 2021
Published online: 10 Jun 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article