Autism spectrum disorder (ASD) is one of the most notable neurodevelopmental disorders that gained major notification among parents, clinicians and even in researchers in the current era. The early identification of autism is a much needed support for parents and clinicians. The proposed methodology aims in building a computational model for such easy and early diagnosis by analyzing and finding the correlations between features-to-class and feature-to-feature so as to maximize the former and minimize the latter. The correlation between features is analyzed using (i) chi square computation technique in filter method and (ii) information gain. On analyzing the correlations, the resultant attributes of every technique are trained separately under the standard linear SVM classifier and then tested for the models performance and accuracy. There are two major contributions of the proposed work; Method 1: to build a model that takes optimized features extracted from the chi square and information gain analysis from questionnaires on the application of genetic algorithm (GA). The optimized features are then trained and tested to classify autism in support of SVM linear classifier. Method 2: to build a model based on the application of back-propagation feed forward neural network to classify the presence of autism. The paper ensures better and faster convergence of the positive class label of autism with maximized accuracy, specificity, performance and minimized error. The novelty of the paper lies in the fact of extracting important features for modeling so as to make a prior analysis by any parents at home before approaching clinicians which supports the early intervention of autism. © 2020, Springer Nature Singapore Pte Ltd.