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Neuromuscular disorder is a complex progressive health problem which results in muscle weakness and fatigue. In recent years, with emergence and development of machine learning- and sequencing-driven technologies, the prediction of neuromuscular disorders could be made on the basis of gene expression for accurate diagnosis of disease. The intent is to correctly distinguish the patients affected from neuromuscular disorder from the healthy one with the help of various classification methods used in machine learning. In this paper, we proposed a novel feature selection method which applies deep learning method for grouping the outputs generated through various classifiers. The feature selection is performed on the basis of integrated Bhattacharya coefficient and genetic algorithm (GA) where fitness is computed on the basis of ensemble outputs of various classifiers which is performed using deep …