可解释的基于集群的预测框架,用于早期诊断自闭症谱系障碍,使用行为生物标志物
Menwa Alshammeri1,2, Zulfiqar Ahmad3, Mamoona Humayun4
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|December 30, 2025
概括
这项研究引入了一个可解释的AI框架,用于早期自闭症谱系障碍 (ASD) 诊断,使用幼儿行为数据. 随机森林模型实现了98.85%的准确性,识别了及时干预的关键指标.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 发展心理学 发展心理学
背景情况:
- 自闭症谱系障碍 (ASD) 呈现出早期行为不规则,挑战及时诊断.
- 诊断资源有限和复杂的表现阻碍了早期检测.
研究的目的:
- 开发一个可解释的机器学习框架,用于早期的ASD诊断.
- 利用来自幼儿查数据的行为生物标志物来改进检测.
主要方法:
- 综合无监督学习 (DBSCAN,K-means) 用于模式识别.
- 应用预测模型:逻辑回归 (LR),随机森林 (RF),支持向量机 (SVM).
- 采用SHAP分析,以实现模型透明度和临床可解释性.
主要成果:
- 随机森林 (RF) 模型实现了最高的准确性,达到98.85%.
- 支持矢量机 (SVM) 和物流回归 (LR) 模型的准确性分别为97.70%和90.53%.
- 可解释性分析确定了ASD风险的临床相关行为指标.
结论:
- 该框架提高了早期发现自闭症的诊断准确性.
- 促进可解释的人工智能,将其纳入临床神经精神病学评估管道.
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