用3D步态视频+智能优化算法，100%准确识别自闭症。
Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video Dataset
- 结合机器学习与仿生优化算法提取关键特征
- 最佳组合实现100%分类准确率，计算更快
- 适合医疗诊断与人工智能交叉研究者
自闭症谱系障碍（ASD）是一种慢性神经发育障碍，表现为重复行为及社交沟通能力缺陷。尽管症状明显，仍有许多患者未被确诊。本文提出一种基于三维行走视频数据集的ASD检测方法，采用监督学习分类器配合仿生优化算法进行特征提取。通过引入排序系数确定初始优质粒子，显著降低计算时间，提升效率与准确性。实验表明，随机森林分类器与引力搜索算法结合时，分类准确率达到100%。该方法在其他数据集上应用也展现出更强鲁棒性与泛化能力。高精度与低计算成本使该框架对医学与学术领域均有重要贡献，为未来ASD诊断提供新基础。
原文摘要 · Abstract (English)
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.
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