Key points are not available for this paper at this time.
Abstract: This study presents a comprehensive analysis of the application of machine learning techniques for the prediction of autism spectrum disorder (ASD). The dataset used in this research comprises a range of demographic, behavioral, and diagnostic features. The study focuses on the use of various machine learning algorithms, including limited decision trees, support vector machines, and neural networks, to predict the likelihood of ASD in individuals. In addition, engineering and feature selection strategies are investigated to determine the most pertinent characteristics for precise prediction. Metrics like accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve are used to assess how well various algorithms perform. Results show promising potential for the utilization of machine learning models in predicting ASD, with certain algorithms exhibiting superior predictive capabilities. The findings of this study provide valuable insights into the potential use of machine learning in the early detection and intervention of autism, ultimately contributing to improved outcomes for individuals on the autism spectrum
Sumathi et al. (Mon,) studied this question.