Identification of Autism Spectrum Disorder using Deep Neural Network

Abstract

One of the acute neuro developmental disorders throughout the world today is the Autism Spectrum disorder (ASD). It is lifelong disorder which affects the behaviour and communication skill of an individual. According to world health organization 2019 report, the number of individuals diagnosed with ASD is increasing creating a threat as it is analogous to significant health care cost. Early recognition can considerably reduce the effect. In order to get rid of the time consuming and expensive diagnosis procedures for ASD, a mobile based ASD screening tool known as ASDTest app was developed. The app recorded over 1400 number of instances covering toddler, child, adolescent and adult. It is available publicly in Kaggle and UCI Machine Learning repository for research purpose. The paper gives a new approach for identification of ASD using a deep classifier. The ASD identification works in the following steps. Feature analysis explains ASD traits thereby improving the efficiency of screening process. Further, Machine Learning (ML) classifier models report ASD class type with evaluation parameters. In this analysis, an attempt is made for the incorporation of Principal Component Analysis (PCA) for feature dimension reduction followed by the usage of Deep Neural Network (DNN) for classification of ASD class type. The data upon which the techniques are applied are collected from Kaggle and UCI ML repository. The experiment result indicates that, PCA in combination with DNN provide clinically acceptable output for effective ASD identification.