Maternal Health Risk Prediction using Machine Learning

Abstract—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.

Index Terms—Maternal Health, Machine Learning, XGBoost, Risk Prediction, Explainable AI