%0 Journal Article
	%A Norah Alshahrani and  Abdulaziz Almaleh
	%D 2023
	%J International Journal of Health and Medical Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 194, 2023
	%T Using Machine Learning Techniques for Autism Spectrum Disorder Analysis and Detection in Children
	%U https://publications.waset.org/pdf/10012974
	%V 194
	%X Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.
	%P 46 - 53