Artificial Intelligence-Driven Approach for Predicting Maternal Health Risk Factors

The development of machine learning has the potential to significantly improve the identification and treatment of pregnancy-related risks in maternal health. This work uses an extensive dataset to create reliable models that can predict possible health issues with accuracy and reliability. Our models are highly predictive and have transparency and interpretability in their results because of our creative feature engineering, such as featurewiz and chi-square test, and meticulous data processing. The models underwent extensive validation procedures, and in three different categories, each proved better evaluation metrics results alongside accuracy levels above 99% in each class. Ensuring the durability and stability of the models, this remarkable degree of accuracy is further enhanced by ensemble techniques and hyperparameter tuning. Incorporating Explainable AI (XAI) methods, such as LIME and SHAP, has proven crucial in elucidating the models’ decision-making process, fostering trust, and enabling their possible use in healthcare environments. The results highlight the potential of ML in predicting maternal health risks and establishing solid foundations for future integration with Medical Cyber-Physical Systems (MCPS), which has the potential to transform the field of predictive analytics in prenatal care completely.