Efficient Deep Learning and Machine Learning Models for Early-stage Identification of Autism Spectrum Disorder in Toddlers: Evidence from Saudi Arabia

Abstract

Autism Spectrum Disorder (ASD) is a type of developmental disorder that can have notable effects on a person’s cognitive abilities, language skills, ability to recognize objects, social interactions, and communication skills. The primary etiology of this condition is attributed to genetics, and prompt detection and intervention may mitigate the potential for the individual to face exorbitant healthcare expenses and protracted diagnostic procedures. A machine learning (ML) and deep learning architecture was developed with the capability to effectively analyze datasets of autistic toddlers, accurately classifying and identifying ASD traits. To explore the feasibility of predicting and analyzing ASD characteristics across various age cohorts, we employed multiple supervised ML models, namely support vector machine (SVM), k-nearest neighbors algorithm, and decision tree, and deep learning models, such as long short-term memory (LSTM). In this study, we analyzed the ASD screening dataset of toddlers from Saudi Arabia. The ASD screening datasets of toddlers from Kaggle were used to test these models. The first dataset includes 1054 instances and 19 toddler-related features, while the remaining datasets consist of 16 features, 507 instances, 165 normal, and 141 ASD cases. We report baseline results of behavior classification using ML and DL approaches. The SVM approach achieved 100% accuracy, whereas the LSTM approach attained 100% accuracy in terms of the accuracy metric. The developed system demonstrates the efficacy of the ASD system in detecting ASD toddlers in Saudi Arabia. Furthermore, the ASD system has the potential to assist parents in examining their children at an early stage.