Multimodal feature analysis for automated neonatal jaundice assessment using machine learning.

Objective: Neonatal jaundice monitoring is resource-intensive. Existing artificial intelligence methods use image or clinical data, but none systematically combine both or compare feature contributions. This study fills that gap by extracting and analyzing multimodal features on a large dataset, identifying an optimal feature set for accurate, accessible jaundice assessment. Materials and Methods: This study collected clinical data and skin images from 3 body regions of 633 neonates, generating 460 features across 4 categories. Four tree-based models were used to predict total serum bilirubin levels and feature importance analysis guided the selection of an optimal feature set. Results: The optimal performance was achieved using the Light Gradient Boosting Machine (LGBM) model with 140 selected features, yielding a root mean square error (RMSE) of 2.0477 mg/dL and a Pearson correlation of 0.8435. This represents a performance gain of over 10% in RMSE compared to models using only a single data modality. Moreover, selecting the top 30 features based on SHapley Additive exPlanation (SHAP) allows for a substantial reduction in data dimensionality, while maintaining performance within 5% of the optimal model. Discussion: Color features contributed over 60% of the total importance, with clinical data adding more than 25%, led by hour of life. Light temperature also affected predictions, while texture features had minimal impact. Among body regions, the abdomen provided the most informative signals for jaundice severity. Conclusion: The proposed algorithm shows promise for real-world use by enabling timely, automated jaundice assessment for families, while also offering insights for future research and broader medical applications.