Early Autism Spectrum Disorder Screening in Toddlers: A Comprehensive Stacked Machine Learning Approach

Abstract

In this paper, we have introduced a study that addresses the critical need for early detection of Autism Spectrum Disorder (ASD) in toddlers. ASD is characterized within the context of its profound impact on early childhood development, emphasizing the urgency of identifying it as early as possible. To achieve this, the study employs a diverse set of base models, including Logistic Regression, KNN, Decision Trees (DT), Support Vector Machines (SVM), and Neural Networks (NN), among others, as part of its methodology. One key aspect of the methodology is the meticulous execution of feature selection using these models. The focus is on identifying the top four features that are most indicative of ASD for subsequent training. By leveraging various machine learning algorithms, the study aims to develop accurate predictive models for early ASD detection.The results of the study are promising, with the models achieving high levels of accuracy. The models with the highest accuracy are identified, and a stacking technique is systematically applied, combining the strengths of different classifiers to further enhance performance. The most significant finding of the study is the exceptional accuracy rate of 99.148% achieved by the proposed approach. This high accuracy rate underscores the efficacy of the methodology in early ASD detection. By accurately identifying ASD in toddlers at an early stage, the study demonstrates the potential for timely intervention and support for affected children, ultimately improving their long-term outcomes and quality of life.