Norah Alshahrani and Abdulaziz Almaleh
Using Machine Learning Techniques for Autism Spectrum Disorder Analysis and Detection in Children
46 - 53
2023
17
2
International Journal of Health and Medical Engineering
https://publications.waset.org/pdf/10012974
https://publications.waset.org/vol/194
World Academy of Science, Engineering and Technology
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¨ve 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.
Open Science Index 194, 2023