Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental condition characterized by repetitive behaviors and impairments in social and communication skills. Despite the clear manifestation of these symptoms, many individuals with ASD remain undiagnosed. This paper proposes a methodology for ASD detection using a three-dimensional walking video dataset, leveraging supervised machine learning classification algorithms combined with nature-inspired optimization algorithms for feature extraction. The approach employs supervised classifiers to identify ASD cases, while nature-inspired optimization techniques select the most relevant features, enhanced by the use of ranking coefficients to identify initial leading particles. This strategy significantly reduces computational time, thereby improving efficiency and accuracy. Experimental evaluation with various algorithmic combinations demonstrates an exceptional classification accuracy of 100% in the best case when using the Random Forest classifier coupled with the Gravitational Search Algorithm for feature selection. The methodology's application to additional datasets promises improved robustness and generalizability. With its high accuracy and reduced computational requirements, the proposed framework offers significant contributions to both medical and academic fields, providing a foundation for future advances in ASD diagnosis.

翻译：自闭症谱系障碍（ASD）是一种以重复性行为及社交与沟通技能缺陷为特征的慢性神经发育疾病。尽管这些症状表现明显，许多ASD患者仍未被确诊。本文提出了一种利用三维步态视频数据集进行ASD检测的方法，该方法结合监督式机器学习分类算法与自然启发优化算法进行特征提取。该方案采用监督分类器识别ASD病例，同时通过自然启发优化技术筛选最相关特征，并借助排序系数识别初始引导粒子以增强性能。此策略显著减少了计算时间，从而提升了效率与准确性。采用不同算法组合的实验评估表明，当使用随机森林分类器结合引力搜索算法进行特征选择时，在最优情况下实现了100%的卓越分类准确率。该方法在其他数据集上的应用有望提升其鲁棒性与泛化能力。凭借高准确率与低计算需求，所提出的框架为医学与学术领域作出了重要贡献，为ASD诊断的未来进展奠定了基础。