Past, present and future of gene feature selection for breast cancer classification – a survey. (6th June 2022)

• Record Type: Journal Article
• Authors: Chowdhary, Chiranji Lal; Khare, Neelu; Patel, Harshita; Koppu, Srinivas; Kaluri, Rajesh; Rajput, Dharmendra Singh
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 2(2022)
• Page Start: 140
• Page End: 153
• Publication Date: 2022-06-06
• Subjects: breast cancer -- classification -- gene selection -- k-nearest neighbour -- KNN -- microarray analysis -- support vector machine -- SVM; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

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.