一个全面的可解释的机器学习框架,用于轻度认知障碍和阿尔茨海默病诊断
Maria Eleftheria Vlontzou1, Maria Athanasiou2, Kalliopi V Dalakleidi2
1Faculty of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece. mvlontzou@biosim.ntua.gr.
Scientific reports
|March 12, 2025
概括
本研究引入了一种可解释的机器学习框架,用于诊断轻度认知障碍 (MCI) 和阿尔茨海默病 (AD). 它通过使用脑MRI和遗传数据来提高诊断准确性和模型解释性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 轻度认知障碍 (MCI) 和阿尔茨海默病 (AD) 带来了重大的诊断挑战.
- 现有的MCI/AD诊断机器学习模型往往缺乏可靠的解释性.
- 整合神经成像和遗传数据对于理解疾病病理生理学至关重要.
研究的目的:
- 开发一个可解释的机器学习 (ML) 框架,用于增强MCI和AD的诊断.
- 在临床决策中确保ML模型解释的稳定性和可靠性.
- 确定关键的神经成像和与MCI/AD进展相关的遗传生物标志物.
主要方法:
- 利用了来自阿尔茨海默病神经成像计划 (ADNI) 的数据集,包括大脑MRI体积数据和遗传信息.
- 采用集体学习方法来解决数据集中的阶级不平衡问题.
- 基于杆的归因 (例如,SHAP) 和基于反事实的可解释性方法用于各种解释.
- 实施了一种统一方法,将SHAP和反事实结合起来,以评估可解释性的稳定性.
主要成果:
- 性能最好的ML模型实现了87.5%的平衡精度和90.8%的F1得分.
- 归因方法确定了与MCI/AD风险相关的显著体积和遗传特征.
- 统一方法证明了这些特征在诊断中的必要性和充分性.
结论:
- 拟议的可解释的ML框架显著提高了MCI/AD诊断.
- 强大的解释性方法为疾病机制和生物标志物提供了关键的见解.
- 这种方法有望改善早期检测和个性化治疗策略.
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