Past, present and future of gene feature selection for breast cancer classification - a survey

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.