Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning.

This decision ensures that the model is both efficient and practical for real-world implementation, particularly in resource-constrained clinical environments where quick predictions and model interpretability are essential In the present study, XGBoost (AUC = 0.823) demonstrates a slight advantage in these metrics compared to Random Forest (AUC = 0.817), though the superiority may not be highly significant.

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