一种新的诊断自闭症谱系障碍 (DASD) 策略,使用基于血液测试的组合诊断方法
Asmaa H Rabie1, Ahmed I Saleh1
1ComputerEngineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Health information science and systems
|August 17, 2023
概括
这项研究引入了一种新的AI驱动的战略,用于早期发现自闭症谱系障碍 (ASD). 诊断自闭症谱系障碍 (DASD) 策略使用先进的数据过和组合诊断方法准确地识别儿童的ASD.
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
- 机器学习用于医学诊断
背景情况:
- 自闭症谱系障碍 (ASD) 是一种复杂的神经发育状况,影响社会沟通和行为.
- 早期发现ASD对于及时干预和管理至关重要,但确切的诊断仍然具有挑战性.
- 现有的诊断方法可能缺乏广泛早期查所需的速度和精度.
研究的目的:
- 介绍一种基于人工智能 (AI) 的新策略,即诊断自闭症谱系障碍 (DASD),用于在儿童中快速准确地检测ASD.
- 通过采用两层方法来提高诊断准确性:数据过层 (DFL) 和诊断层 (DL).
- 引入一个集体诊断方法 (EDM) 来精确识别ASD.
主要方法:
- 数据过器层 (DFL) 使用二进制灰狼优化 (BGWO) 进行特征选择,并使用二进制遗传算法 (BGA) 进行异常值拒绝.
- 诊断层 (DL) 使用集体诊断方法 (EDM),采用增强的K-最近邻居 (EKNN).
- EKNN集成K-最近邻居 (KNN),天真贝叶斯 (NB) 和黑猩猩优化算法 (COA) 来进行数据转换,缩小和分类.
主要成果:
- 拟议的DASD策略在ASD血液测试数据集上实现了高性能.
- 关键性能指标包括精度为0.93,错误率为0.07,F1测量为0.79.
- 该策略的快速实施时间为1.5秒,超过了现有方法.
结论:
- DASD策略为早期和准确的自闭症谱系障碍诊断提供了一种卓越的方法.
- 先进的特征选择,数据清理和组合学习的结合显著改善了诊断结果.
- 这种人工智能驱动的方法有望提高自闭症诊断率,并促进及时干预.
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