AI RESEARCH
Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
arXiv CS.LG
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ArXi:2603.06758v1 Announce Type: new Alzheimer disease (AD) diagnosis and prognosis increasingly rely on machine learning (ML) models. Although these models provide good results, clinical adoption is limited by the need for technical expertise and the lack of trustworthy and consistent model explanations. SHAP (SHapley Additive exPlanations) is com-monly used to interpret AD models, but existing studies tend to focus on explanations for isolated tasks, providing little evidence about their robustness across disease stages, model architectures, or prediction objectives.