A hybrid gene selection model for molecular breast cancer classification using a deep neural network

Abstract

Microarray-based gene expression outlining portrays a dominant part in a healthier understanding of breast cancer. From the large quantum of data, a powerful technique is required to understand and extract the required information. The molecular subtype extraction is one of such important information regarding breast cancer, which is very crucial in defining its treatment strategy. This manuscript has formulated a deep neural network-based model for molecular classification of breast cancer. The proposed model exploits pre-processing steps along with the hybrid approach of filter and wrapper-based feature selection to extract relevant genes. The extracted genes are evaluated using various machine learning approaches where it is observed that selected features are successful in solving this multiclass problem. Using the proposed hybrid model, we have achieved the highest accuracy with six microarray datasets. The model outperforms magnificently in standings of sensitivity, f-measure, specificity, MCC and recall. Hence, deep neural network is identified as the best efficient classifiers concluding brilliant performance with all the selected microarray gene expression datasets for a range of selected genes.