Machine Learning‐Based Risk Stratification Tool for Hearing Loss in High‐Risk Neonates

OBJECTIVE: This study aims to develop and evaluate a machine learning-based risk stratification tool for predicting hearing loss in high-risk neonates using clinical risk factors to support targeted early identification. METHODS: A total of 270 infants, 105 with hearing loss, 165 with normal hearing who had passed initial screening but possessed clinical risk factors were retrospectively analyzed. Clinical variables included prematurity, low birth weight, hyperbilirubinemia, phototherapy, NICU stay duration, and family history. Five models (Random Forest, XGBoost, CatBoost, K-nearest neighbors, and Logistic Regression) were developed using an 80/20 train-test split and stratified 5-fold cross-validation. Additionally, a web-based clinical decision support tool was developed using the Streamlit framework to provide real-time risk assessment. RESULTS: The XGBoost model achieved the highest performance with 85.2% accuracy and an AUC of 87.1%. SHAP analysis identified NICU stay duration and positive family history as the most influential predictors for neonatal hearing loss. CONCLUSION: Machine learning models, particularly XGBoost, provide robust risk stratification for high-risk neonates. Rather than replacing universal screenings, these tools can complement existing programs by identifying high-risk infants who require prioritized diagnostic follow-up and closer clinical monitoring. The developed web application (available at https://newbornhearing.streamlit.app/) offers a practical interface for clinical use.