Maternal Health Risk Prediction using Machine Learning

Pregnancy-related complications remain a lead- ing cause of maternal and neonatal mortality, particularly in resource-constrained settings where timely clinical intervention is often unavailable. Identifying women at elevated risk before complications escalate is therefore a pressing need. This paper presents a multi-class classification framework that predicts maternal risk levels — Low, Mid, or High — from routine physiological measurements: age, blood pressure, blood glucose, body temperature, and heart rate. Working with a dataset of 1014 records, we applied label encoding, StandardScaler normalization, and stratified splitting as part of preprocessing. Three classifiers were trained and compared: Random Forest, Support Vector Machine, and XGBoost. After hyperparameter tuning, the XGBoost model reached 86.7% accuracy, a cross- validated Macro F1-score of 0.82, and a high-risk class recall of 0.88. To make predictions interpretable for clinical users, SHAP values were incorporated to expose the contribution of each feature to individual risk decisions.