Enhancing Early Detection of Neonatal Hearing Loss: A Machine Learning Risk Stratification Approach
Highlight
- 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.
Study Background
MedXY registered readers
Sign in free to continue reading
Create or use your MedXY account to unlock the complete article.
This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.