Ensemble-based Machine Learning Classification for Early Detection of Autism Spectrum Disorder

<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">__</sup>A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health conditions, diagnosing it can take a long time. The goal of this study is to develop a categorization model that can forecast the risk of ASD in toddlers, kids, teens, and adults. To improve speed, we used a variety of machine learning methods and ensembled them. Four publicly accessible non-clinical ASD screening datasets from Kaggle and the U CI library were used to assess the models. These datasets included 1,054 cases with 19 features for toddlers and different characteristics for older age groups. The classifiers' accuracy was almost 99%, according to the results.