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Data Analysis With Shapley Values For Automatic Subject Selection in Alzheimer's Disease Data Sets Using Interpretable Machine Learning

2021-03-01

Abstract excerpt

<title>Abstract</title> <p><bold>Background:</bold> The prediction of whether Mild Cognitive Impaired (MCI) subjects will prospectively develop Alzheimer's Disease (AD) is important for the recruitment and monitoring of subjects for therapy studies. Machine Learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to noisy data sets. Additional noise is introduced...

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Literature Corpus work
f2ae6ce2-e6cf-54d6-b5ca-6caf1a509ef5
DOI
10.21203/rs.3.rs-245707/v1
Open publication

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Data Analysis With Shapley Values For Automatic Subject Selection in Alzheimer's Disease Data Sets Using Interpretable Machine LearningDOI 10.21203/rs.3.rs-245707/v1
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