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在儿童和成人中使用机器学习检测自闭症谱系障碍 (ASD)

Muhammad Shoaib Farooq¹, Rabia Tehseen², Maidah Sabir¹

¹Department of Artificial Intelligence, University of Management and Technology, Lahore, 54000, Pakistan.

Scientific reports

|June 13, 2023

概括

联合学习 (FL) 增强了早期自闭症谱系障碍 (ASD) 检测. 这种新的方法在儿童中达到98%的准确性,在成年人中达到81%,改善了诊断能力.

科学领域:

神经科学和发育心理学
在医疗保健中的机器学习和人工智能

背景情况:

自闭症谱系障碍 (ASD) 是一种复杂的神经发育状况,影响社会,认知和沟通技能.
早期诊断ASD对于减轻长期影响和改善干预结果至关重要.
传统的诊断方法可能是有限的,需要先进的计算方法来及时检测.

研究的目的:

应用联合学习 (FL) 来准确和早期发现儿童和成人自闭症谱系障碍 (ASD).
评估物流回归和支持向量机分类器在FL框架内用于ASD检测的有效性.
开发一个元分类器,以确定最准确的ASD检测方法.

主要方法:

采用联合学习 (FL),在ASD患者数据集上本地训练后勤回归和支持矢量机器模型.
来自本地分类器的结果被汇总到中央服务器上进行元分类器培训.
四个不同的ASD数据集,每个数据集有600多个记录,用于特征提取和模型训练.

主要成果:

拟议的FL模型在儿童中检测ASD时达到98%的高精度.
该模型在检测成年人ASD时显示出81%的准确性.
超分类器成功地确定了用于ASD检测的最准确的本地分类方法.

结论:

联合学习为早期自闭症谱系障碍检测提供了一个有希望,准确和保护隐私的方法.
混合方法将本地ML分类器与中央元分类器相结合,提高了ASD的诊断准确性.
这种基于FL的策略显示了改善不同年龄组的ASD诊断的巨大潜力.

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