An interpretable ML framework combining bagging with one-versus-one classification and multiple XAI methods achieves 87.5% balanced accuracy and 90.8% F1 on MCI/AD diagnosis in the ADNI cohort.
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A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
An interpretable ML framework combining bagging with one-versus-one classification and multiple XAI methods achieves 87.5% balanced accuracy and 90.8% F1 on MCI/AD diagnosis in the ADNI cohort.