MaternaInsight: Maternal Health Risk and Fetal Health Monitoring System Using Machine Learning

Pregnancy complications still constitute an enormous threat to both mothers and babies around the world. According

to WHO estimates, about 712 women die daily from pregnancy and delivery-related complications with more than 90% of cases

occurring in low- and lower-middle-income countries [1]. Conventional methods of assessing maternal and fetal health risk

depend on human interpretation of clinical measurements and Cardiotocography (CTG) signals, which can be tedious and timeconsuming while being prone to significant inter-rater variability [2]. In this work, we propose a combined clinical decision

support system called MaternaInsight which involves machine-learning models for both maternal and fetal health risk

prediction. Maternal health risk prediction model identifies three risk categories – Low, Mid, and High based on LightGBM

classifier optimized with Optuna and rebalanced with SMOTETomek, resulting in 90.64% of test accuracy (macro-F1=90.62%).

Fetal classification is accomplished using a Stacking Ensemble approach with a logistic regression model as the meta-classifier,

and the model is trained using 39 CTG-derived features, yielding an accuracy of 94.13% and macro-F1 of 88.66% on test data.

Both classifiers are trained on data following the strict sequence of Split-Scale-Balance to avoid leakage. They also include

interpretability of individual predictions using SHAP, making them clinically explainable. The entire pipeline is encapsulated in

a four-page Streamlit web app containing a Clinical Reference and Performance Dashboard.