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

Yunfeng Liang¹, Lin Zou¹, Millie Ming Rong Goh¹

¹Data aNalytics & AI, Synapxe Pte Ltd, Singapore 139691, Singapore.

|December 11, 2025

Summary

This study developed an AI model combining clinical and image data for accurate neonatal jaundice assessment. The model significantly improves accuracy, offering a resource-efficient solution for monitoring infant health.

Keywords:

feature generation feature importance machine learning multimodal data fusion neonatal jaundice

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Area of Science:

Neonatal care
Artificial Intelligence
Medical imaging

Background:

Neonatal jaundice monitoring is resource-intensive.
Current AI methods use either image or clinical data, but not both.
A systematic comparison of multimodal feature contributions is lacking.

Purpose of the Study:

To develop an AI model combining multimodal features for accurate neonatal jaundice assessment.
To identify an optimal feature set for jaundice prediction.
To compare the contributions of different data modalities and features.

Main Methods:

Collected clinical data and skin images from 633 neonates.
Generated 460 features across 4 categories.
Utilized tree-based models, including Light Gradient Boosting Machine (LGBM), and SHapley Additive exPlanation (SHAP) for feature importance analysis.

Main Results:

The LGBM model with 140 features achieved an RMSE of 2.0477 mg/dL and a Pearson correlation of 0.8435.
Combined multimodal data improved performance by over 10% compared to single modalities.
Top 30 SHAP features reduced dimensionality while maintaining performance within 5% of the optimal model.

Conclusions:

Color features and clinical data (hour of life) are most important for jaundice assessment.
The abdomen provided the most informative skin signals.
The proposed algorithm offers a promising, automated solution for timely jaundice assessment.