An Integrated AI-Driven Framework for Predictive and Preventive Maternal Health: A Multi-Domain Approach
DOI:
https://doi.org/10.15662/IJEETR.2026.0802081Keywords:
Machine Learning, Gestational Diabetes Mellitus, Down SyndromeAbstract
Maternal and prenatal healthcare involves complex physiological and clinical factors that require early, accurate, and reliable risk assessment to prevent adverse pregnancy outcomes. Recent advances in Artificial Intelligence (AI) have demonstrated strong potential for supporting predictive maternal healthcare; however, existing approaches are often disease-specific, lack interpretability, and face challenges related to data heterogeneity and privacy. This paper proposes an Integrated AI-Driven Framework for Predictive and Preventive Maternal Health that unifies multimodal data processing, explainable machine learning, and secure clinical deployment. The framework incorporates demographic, clinical, and laboratory data to predict maternal risks such as gestational diabetes mellitus using ensemble-based models enhanced with SHAP-based explainability. A Clinical Decision Support System translates predictions into actionable recommendations. Experimental results on publicly available maternal datasets demonstrate improved predictive performance, achieving an accuracy of 92.6% and an AUC of 0.96. The proposed framework offers a scalable, interpretable, and privacy-aware solution for intelligent maternal healthcare.
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