Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning

• Published in: Alzheimer's research & therapy Vol. 13; no. 1; pp. 155 - 30
• Main Authors: Bloch, Louise, Friedrich, Christoph M.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.09.2021; BioMed Central; BMC
• Subjects: ADNI; AIBL; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; Australia; Biomarkers; Classification; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Computer-aided medical diagnosis; Data Analysis; Data mining; Data Shapley; Datasets; Dementia; Diagnosis; Discriminant analysis; Game theory; Humans; Interpretability; Machine Learning; Magnetic Resonance Imaging; Methods; Neural networks; Noise; Older people; Shapley values; Australia; Germany; ADNI; Shapley values; Alzheimer’s disease; Interpretability; Mild cognitive impairment; Machine learning; AIBL; Data Shapley

For the recruitment and monitoring of subjects for therapy studies, it is important to predict whether mild cognitive impaired (MCI) subjects will prospectively develop Alzheimer's disease (AD). Machine learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneou...