What question did this study set out to answer?

The main aim is to develop an explainable AI framework for accurately predicting birth weight through regression.

February 24, 2026Discover Artificial Intelligence0 citationsOpen Access

A regression-based multi-task machine and deep learning framework for explainable AI driven birth weight prediction in maternal and neonatal care

AAAnika Bintee AftabBangladesh University of Engineering and Technology NLNishat Anjum LeaBangladesh University of Engineering and Technology MIMd Rakibul IslamAhsanullah University of Science and Technology

Key Points

The main aim is to develop an explainable AI framework for accurately predicting birth weight through regression.
Evaluated various machine learning and deep learning models on a public prenatal dataset.
Applied a stacking ensemble of Feedforward Neural Networks, Gradient Boosting, and LightGBM.
Implemented explainable AI techniques like SHAP, LIME, and Permutation Feature Importance to enhance interpretability.
Conducted complementary analyses using Random Forest, XGBoost, and K-Means for classification and clustering.
Achieved an R2 of 0.5026 and MSE of 0.8924 with the stacking ensemble model.
Demonstrated peak classification accuracy of 92% using ensemble methods.
Integrated XAI techniques successfully identified key prenatal factors affecting birth weight.

Abstract

Predicting birth weight is a critical task in prenatal care, directly influencing maternal and neonatal health interventions and outcomes. While most prior studies have focused primarily on categorical classification of birth weight into discrete ranges (e.g., low, normal, high), the clinically more valuable task of predicting the exact birth weight through regression has received relatively little attention. Accurate regression-based prediction enables more precise risk stratification, individualized care planning, and timely intervention, which are essential for improving neonatal health. In this study, we address this gap by systematically evaluating a wide range of machine learning (ML), deep learning (DL), and ensemble regression models on a publicly available prenatal dataset. Among the models tested, a stacking ensemble combining Feedforward Neural Networks (FNN) and Gradient Boosting as base learners with LightGBM as the meta-model achieved the highest regression performance (R2: 0.5026, MSE: 0.8924), outperforming previously reported approaches. To ensure clinical interpretability, explainable AI (XAI) techniques, including SHAP, LIME, and Permutation Feature Importance, were incorporated to identify key prenatal factors contributing to birth weight, providing actionable insights for healthcare providers. Complementary experiments on classification into five clinically significant groups (using Random Forest, XGBoost, and LightGBM, achieving peak accuracy of 92%) and unsupervised clustering (K-Means, Bootstrap K-Means, DBSCAN, enhanced with PCA) demonstrated the robustness and versatility of the framework. This study emphasizes three major contributions: (i) highlighting regression-based birth weight prediction as a novel and underexplored approach, (ii) achieving superior predictive accuracy over existing methods, and (iii) integrating explainable AI to bridge predictive power with clinical interpretability. Overall, the proposed multi-task framework provides precise, interpretable, and clinically actionable predictions, representing a meaningful advancement in prenatal data analytics and maternal-neonatal healthcare.

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Cite This Study

Aftab et al. (Sun,) studied this question.

synapsesocial.com/papers/699d3ff8de8e28729cf64db4 https://doi.org/https://doi.org/10.1007/s44163-026-01012-w

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