Detection of Autism Spectrum Disorder (ASD) Symptoms using LSTM Model

Abstract

Autistic children will often exhibit certain behaviors that are unique to them and that are not typical of neurotypical children. Parents will become familiar with these patterns over time and will be able to use this knowledge to answer questions about their child's behavior. Deep learning models are very useful to solve critical problems in the healthcare domain. Detection of ASD at the early age of a child is a challenging task. Recent research reveals that there is an increasing trend of ASD among children. Communication, eye contact, social behavior, and education are very poor for those who suffer from ASD. The proposed research work has been done to detect ASD symptoms in a child. Data has been collected from the various autism groups from social sites and organizations that are working on special children. A Deep learning model like the Long-Short Term Memory (LSTM) model has been used to detect the sentiment of parents’ dialog. LSTM is the most popular deep learning model that can able to solve complex natural language problems. The proposed LSTM model has been trained with prepared data and accuracy is 97% according to the prepared data.