Autism Spectrum disorder Detection in Toddlers and Adults Using Deep Learning

Autism spectrum disorder includes symptoms like anxiety, depressive disorders, and epilepsy because of its impact on relationships, learning, and employment. Since no confirmed treatment and diagnosis are available, the emphasis is on improving an individual’s capacities through symptom mitigation. This work investigates autism screening for adults and toddlers utilizing deep learning. We investigated models for feature prediction and fused these predictions with the original dataset to be trained with deep long short-term memory (DLSTM). Features are fused from the training and testing sets and then combined with the original dataset. Data analysis is carried out to detect anomalies and outliers, and a label encoding technique is utilized to convert the categorical data into numerical values. We hyper-tuned the DLSTM model parameters to optimize and assess significant outcomes. Experimental analysis and results revealed that the proposed approach worked better than the other techniques, yielding 99.9% accuracy for toddlers and 99% for adults.