Comparison of seven machine learning models for predicting prolonged PACU length of stay: performance evaluation and identification of key influencing factors
The XGBoost model developed in this study achieved a relatively high AUC of 0.901, indicating that the model’s ability to confirm prolonged PACU stay remains somewhat insufficient, and can be used as an auxiliary screening tool in clinical rather than a definitive diagnostic tool.
Na Zhu, Xiang Xiong, Xuan-Zhao Wu et al.
· BMC Anesthesiology · 0 citations