XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease
XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease
About this item
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Author / Creator
Yi, Fuliang , Yang, Hui , Chen, Durong , Qin, Yao , Han, Hongjuan , Cui, Jing , Bai, Wenlin , Ma, Yifei , Zhang, Rong and Yu, Hongmei
Publisher
England: BioMed Central Ltd
Journal title
Language
English
Formats
Publication information
Publisher
England: BioMed Central Ltd
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Scope and Contents
Contents
Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework,...
Alternative Titles
Full title
XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_2f7158995ba544838dc87c3f0dbde75f
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_2f7158995ba544838dc87c3f0dbde75f
Other Identifiers
ISSN
1472-6947
E-ISSN
1472-6947
DOI
10.1186/s12911-023-02238-9
