Machine learning-based prediction of intraoperative desaturation risk in pediatric dental sedation using preoperative data

Short summary

An XGBoost machine learning model accurately predicted intraoperative desaturation risk in pediatric dental sedation using only preoperative data, achieving 70.4% accuracy, 69.5% F1-score, and identifying body weight, age, and recent respiratory infections as key risk factors.

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