Aldrees Asma, Ojo Stephen, Wanliss James, Umer Muhammad, Khan Muhammad Attique, Alabdullah Bayan, Alsubai Shtwai, Innab Nisreen
Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
College of Engineering, Anderson University, Anderson, SC, United States.
Front Comput Neurosci. 2024 Oct 21;18:1489463. doi: 10.3389/fncom.2024.1489463. eCollection 2024.
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.
自闭症谱系障碍（ASD）是一种神经发育疾病，其特征在于在认知功能、语言理解、物体识别、与他人互动以及有效沟通方面存在显著挑战。其根源主要是遗传性的，早期识别并及时干预可以减少那些受ASD影响的人进行广泛医疗治疗和冗长诊断程序的必要性。本研究设计了两种类型的实验用于ASD分析。在第一组实验中，作者利用三种特征工程技术（卡方检验、反向特征消除和主成分分析）以及多个机器学习模型来预测幼儿是否患有自闭症。所提出的XGBoost 2.0在使用卡方显著特征时，准确率、F1分数和召回率均达到99%，精确率为98%。在第二种情况下，主要重点转向通过评估自闭症儿童的行为、语言和身体反应来确定适合他们的教育方法。同样，所提出的方法表现良好，准确率、F1分数、召回率和精确率均为99%。在本研究中，还实施了交叉验证技术来检验所提出模型的稳定性，并与先前发表的研究工作进行比较，以显示所提出模型的重要性。本研究旨在使用机器学习技术为自闭症患者制定个性化教育策略，以更好地满足他们的特定需求。
