Development of a machine learning-based risk stratification tool to predict hearing loss in high-risk neonates using clinical parameters.
The XGBoost model demonstrated superior predictive performance with 85.2% accuracy and an area under the curve (AUC) of 87.1%.
NICU stay duration and family history emerged as the most influential risk factors via SHAP interpretability analysis.
A web-based clinical decision support application has been implemented for real-time risk assessment to assist clinicians in early identification and intervention.
