ASD classification for children using deep neural network

Abstract

The recognition of a person in society is based on the behaviour and socio-communicative skills. But some neurodevelopment illness like Autism Spectrum disorder (ASD) highly influences the behaviour and communication skill of an individual. Individuals with such illness need early detection for minimizing its effect. The ASD diagnosis is done by a mobile-based screening app to extract information from all individuals irrespective of age. The information is stored in publicly accessible authenticated research UCI Machine Learning (ML) repository and Kaggle. The proposed approach in this paper is investigated up on child data set gathered from UCI repository. The analysis is done for two distinct cases: complete and missing data via standardization by Mean Standard deviation approach followed by dimension reduction using Diffusion Mapping and finally classification of ASD class utilising Deep Neural Network Prediction and Classification (DNNPC) model. The performance of DNNPC classifier model is validated by distinct performance parameters.