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.
