M-TabNet: A Transformer-Based Multi-Encoder for Early Neonatal Birth Weight Prediction Using Multimodal Data

Birth weight (BW) is a key indicator of neonatal health, and low birth weight (LBW) is linked to increased mortality and morbidity. Early prediction of BW facilitates timely prevention of impaired foetal growth. However, available techniques such as ultrasonography have limitations, including less accuracy when applied before 20 weeks of gestation and operator-dependent variability. Existing BW prediction models often neglect nutritional and genetic influences, and focus mainly on physiological and lifestyle factors. This study presents an attention-based transformer model with a multi-encoder architecture for early ($< 12$ weeks) BW prediction. Our model effectively integrates diverse maternal data, including physiological, lifestyle, nutritional, and genetic data, addressing limitations seen in previous attention-based models such as TabNet. The model achieves a Mean Absolute Error (MAE) of 122 grams and an $R^{2}$ value of 0.94, showing its high predictive accuracy and interoperability with our in-house private dataset. Independent validation confirms generalizability (MAE: 105 grams, $R^{2}$: 0.95) with the IEEE children dataset. To enhance clinical utility, predicted BW is classified into low and normal categories, achieving a sensitivity of 97.55% and a specificity of 94.48%, facilitating early risk stratification. Model interpretability is reinforced through feature importance and SHAP analysis, highlighting significant influences of maternal age, tobacco exposure, and vitamin B12 status, with genetic factors playing a secondary role. Our results emphasize the potential of advanced deep learning models to improve early BW prediction, offering a robust, interpretable, and personalized tool to identify pregnancies at risk and optimize neonatal outcomes.