Multimodal feature analysis for automated neonatal jaundice assessment using machine learning

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.

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.

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.

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.

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.