SHAP analysis of the GBM model.

<p>(A) The SHAP summary plot of the GBM model. (B) Significance analysis of feature importance ranking via SHAP, based on the mean value. (C, D) The force plots offer individualized feature attributions for two representative examples. C: Patients with SAD; D: Patients with Non-SAD.</p>

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Bibliographic Details
Main Author:	Lang Gao (12963876) (author)
Other Authors:	Guang Dong Wang (21728461) (author), Xing Yi Yang (21728464) (author), Shi Jun Tong (21728467) (author), Xu Jie Wang (21728470) (author), Yun Ruo Chen (21728473) (author), Jin Ying Bai (21728476) (author), Ya Xin Zhang (21728479) (author)
Published:	2025
Subjects:	Medicine Biotechnology Developmental Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified strong predictive accuracy shapley additive explanations poses significant challenges online risk calculator online calculator offers medical information mart manage imbalanced data interpretable machine learning gradient boosting machine glasgow coma scale support early identification critically ill patients high clinical applicability risk prediction model related delirium based accurately predicting sad xlink "> sepsis xlink "> early prediction predicting outcomes 120 patients clinical understanding clinical management timely management term cognitive synthetic minority sofa ). sampling technique practical tool poor outcomes optimal model occurs due neurotransmission linked lasso regression inflammatory responses feature selection facilitate real boruta algorithm best one behavioral issues associated delirium
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SHAP analysis of the GBM model.

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