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模糊的评估和可解释的机器学习用于诊断风湿和自身免疫性疾病
Mohammed Fadhil Mahdi1, Arezoo Jahani1, Dhafar Hamed Abd2
1Faculty of Electrical and Computer Engineering, Sahand University of Technology, Tabriz, Iran.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种可解释的AI和模糊逻辑系统,以增强类风湿性疾病的诊断. 最好的模型实现了86.89%的准确性,提高了诊断可靠性和临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 类风湿病学 类风湿病学
背景情况:
- 由于症状重叠,风湿性和自身免疫性疾病存在复杂的诊断挑战.
- 传统的机器学习模型往往缺乏可解释性,阻碍了临床的信任和采用.
- 从多个选项中选择最佳的诊断模型是很困难的.
研究的目的:
- 开发一种结合可解释AI (XAI) 和模糊评估框架的新方法,以改善类风湿病诊断.
- 解决用于医学诊断的机器学习中的可解释性和模型选择挑战.
- 提高风湿病学诊断决策的透明度和可靠性.
主要方法:
- 收集并预处理了12085名患者的数据集,这些患者具有7个类别的14个特征 (包括类风湿性关节炎,狼和正常).
- 评估了12个机器学习模型,使用准确度,精度和F-score等指标.
- 采用了通过意见得分方法 (FDOSM) 的模糊决策与专家输入以及局部可解释的模型不可知解释 (LIME) 来进行模型选择和解释性.
主要成果:
- 通过FDOSM选择的梯度增强模型,以86.89%的精度,87.35%的精度和84.51%的kappa实现了最高的性能.
- XAI的技术,特别是LIME,提供了关键的全球和本地解释,增加了透明度.
- 与传统方法相比,综合方法显示出更高的诊断性能和可解释性.
结论:
- XAI和模糊逻辑的结合提供了一个强大的框架,可以提高风湿病诊断的准确性和可解释性.
- 开发的系统增强了对复杂的自身免疫性疾病的临床决策的信任和可靠性.
- 这种方法对于在医疗保健和医学诊断中推进人工智能应用具有重大潜力.
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