Ensemble recurrent neural network with whale optimization algorithm-based DNA sequence classification for medical applications

The modern data-driven era has facilitated the gathering of large quantities of biomedical and clinical data. The deoxyribonucleic acid gene expression datasets have become a vital focus for the research community because of their capability to detect pathogens via 'biomarkers' or particular modifications in the gene sequence which portray a specific pathogen. Metaheuristic-related feature selection (FS) efficiently filters out only the pertinent genes out of large feature sets to lessen the data storage and computation requirements. This paper embraces the whale optimization algorithm for the FS issue in HD microarray data for the effectual propagation of candidate solutions to reach global optima over sufficient iterations. The chosen data are classified by employing an ensemble recurrent network (ERNN) that retains the amalgamation of long short-term memory, bidirectional long short-term memory, and gated recurrent units. Analysis of this proposed ERNN methodology would be performed by correlating with diverse advanced methodologies, and thus, the ERNN attains 99.59% precision and 99.59% accuracy.