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

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

Abstract Background 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...

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Bibliographic Details
Main Authors: Louise Bloch, Christoph M. Friedrich, for the Alzheimer’s Disease Neuroimaging Initiative
Format: Article
Language:English
Published: BMC 2021-09-01
Series:Alzheimer’s Research & Therapy
Subjects:
Machine learning
Data Shapley
Interpretability
AIBL
ADNI
Shapley values
Online Access:https://doi.org/10.1186/s13195-021-00879-4

Internet

https://doi.org/10.1186/s13195-021-00879-4

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