Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer
This study introduces an explainable machine learning framework using Importance Inversion Transfer to identify eight stable, shared neuroanatomical features across Alzheimer's and Parkinson's diseases, supporting the Neurodegenerative Elderly Syndrome concept and offering a paradigm for early, system-level diagnosis.