用可解释机器学习精准诊断阿尔茨海默病，提升模型可信度。
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
- 融合多种可解释方法，统一分析脑部体积与基因特征。
- 模型达87.5%平衡准确率，F1分数90.8%，性能优异。
- 适合临床医生理解模型决策，辅助早期疾病诊断。
本文提出一种可解释的机器学习框架，用于提升轻度认知障碍（MCI）和阿尔茨海默病（AD）的诊断能力。数据来自阿尔茨海默病神经影像计划（ADNI），包含健康人群及MCI/AD患者基于脑部MRI的体积测量和基因数据。通过集成学习解决类别不平衡问题，并结合基于归因和反事实的可解释性方法，生成与病理生理相关的多样解释。采用一种将SHAP与反事实解释统一的方法评估解释的鲁棒性。最优模型实现87.5%的平衡准确率和90.8%的F1分数。基于归因的方法识别出与MCI/AD风险显著相关的脑体积和基因特征。统一方法揭示了这些特征的必要性与充分性，进一步验证其在诊断中的重要性。
原文摘要 · Abstract (English)
An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through the Alzheimer's Disease Neuroimaging Initiative. The existing class imbalance is addressed by an ensemble learning approach, while various attribution-based and counterfactual-based interpretability methods are leveraged towards producing diverse explanations related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques' robustness. The best performing model yielded 87.5% balanced accuracy and 90.8% F1-score. The attribution-based interpretability methods highlighted significant volumetric and genetic features related to MCI/AD risk. The unification method provided useful insights regarding those features' necessity and sufficiency, further showcasing their significance in MCI/AD diagnosis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。