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Paper details
Number 4 - December 2024
Volume 34 - 2024
Autism spectrum disorder detection in toddlers and adults using deep learning
Sidra Abbas, Stephen Ojo, Moez Krichen, Meznah A. Alamro, Alaeddine Mihoub, Lucia Vilcekova
Abstract
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.
Keywords
autism spectrum disorder (ASD), deep learning, feature fusion, feature prediction, healthcare