A Comparative Study: Deep Learning Approach to Predict Autism Spectrum Disorder Based on Q-Chat-10 Responses

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition that has been observed to have an increasing incidence and significant health-related expenses. The timely identification of these burdens can mitigate their impact; however, current diagnostic protocols are protracted and entail significant expenses. The implementation of machine learning and, more recently, deep learning techniques presents promising remedies to improve ASD screening procedures. The present research introduces a deep learning framework for the purpose of forecasting autism spectrum disorder (ASD) utilizing responses obtained from the Q-Chat-10 questionnaire. The dataset employed in this study comprises 1054 records, encompassing ten behavioral traits and additional individual characteristics. The objective of this study is to improve the precision, efficacy, sensitivity, and specificity of autism spectrum disorder (ASD) predictions by contrasting the performance of a deep learning model with that of conventional machine learning models. The implementation of this technology has the potential to significantly optimize the ASD screening procedure, rendering it more affordable and convenient and ultimately assisting healthcare practitioners in their clinical judgment for prompt ASD identification.