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使用机器学习技术预测和分析自闭症谱系障碍

Muhammad Shuaib Qureshi¹, Muhammad Bilal Qureshi², Junaid Asghar³

¹Department of Computer Science, School of Arts and Sciences, University of Central Asia, Naryn, Kyrgyzstan.

Journal of healthcare engineering

|July 20, 2023

概括

这项研究比较了用于自闭症谱系障碍 (ASD) 预测的机器学习算法. 随机森林算法实现了最高准确率的89.23%,为研究人员提供了一个有前途的框架.

科学领域:

神经发育障碍神经发育障碍
医疗保健中的人工智能
机器学习应用机器学习应用

背景情况:

自闭症谱系障碍 (ASD) 是一种影响社会沟通和行为的终身神经发育状况.
早期识别2-3岁儿童的ASD症状至关重要.
现有的ASD预测研究严重依赖于传统的机器学习算法.

研究的目的:

调查和比较各种机器学习算法用于自闭症谱系障碍预测.
为ASD预测领域的研究人员提供一个集中的框架.
评估基于应用类型,模拟方法,比较方法和输入数据等常见参数的预测模型.

主要方法:

传统机器学习算法的比较,包括支持向量机,随机森林,多层感知器,天真贝叶斯,卷积神经网络和深度神经网络.
使用性能指标 (如准确性,精度和回忆) 验证拟议的模型.
对不同参数的自闭症谱系障碍预测的分析.

主要成果:

与其他传统的机器学习算法相比,随机森林算法表现出卓越的性能.
使用随机森林模型进行ASD预测,获得了89.23%的准确性.
提供了工作流表示,以阐明研究框架的架构.

结论:

随机森林算法对于自闭症谱系障碍预测非常有效.
这项研究提供了一个有价值的集中框架,以指导未来在ASD预测方面的研究.
这些发现强调了机器学习在提高自闭症诊断的准确性和效率方面的潜力.

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