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

ABSTRACT:

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)

SUBMITTER: Liang Y 

PROVIDER: S-EPMC12687590 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature