Optimal Feature Selection and Hybrid Classification for Autism Detection in Young Children

Abstract

Abstract The early detection of autism spectrum disorder acts as a risk in the infants and toddlers as per the increase over the early invention awareness. Hence, this paper has made an effort to introduce a new autism detection technique in young children, which poses three major phases called weighted logarithmic transformation, optimal feature selection and classification. Initially, weighted transformation in the input data is carried out that correctly distinguishes the interclass labels, and it is determined to be the specified features. Because of the presence of numerous amounts of features, the ‘prediction’ becomes a serious issue, and therefore the optimal selection of features is important. Here, for optimal feature selection process, a new Levi Flight Cub Update-based lion algorithm (LFCU-LA) is introduced that can be a modification in lion algorithm. Once the optimal features are selected, they are given to the classification process that exploits a hybrid classifier: deep belief network (DBN) and neural network (NN). Additionally, the most important contributions in the hidden neurons of DBN and NN were optimally selected that paves way for exact detection.