XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease

Abstract Background 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 dia...

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Bibliographic Details
Main Authors: Fuliang Yi, Hui Yang, Durong Chen, Yao Qin, Hongjuan Han, Jing Cui, Wenlin Bai, Yifei Ma, Rong Zhang, Hongmei Yu
Format: Article
Language:English
Published: BMC 2023-07-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Alzheimer’s disease
Machine learning
Imbalanced classes
Multiclassification
Interpretable framework
XGBoost-SHAP
Online Access:https://doi.org/10.1186/s12911-023-02238-9

Internet

https://doi.org/10.1186/s12911-023-02238-9

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