Researchers from BMC Geriatrics used NHANES data to develop an interpretable XGBoost model for predicting post-stroke depression. Combining logistic regression with SHAP analysis, the study identifies key risk factors such as sleep disorders and age, guiding early intervention and improved clinical decisions in stroke recovery.
Q&A
- What is the SHAP algorithm?
- How does the XGBoost model work?
- How can this model improve post-stroke care?
