Background/Objectives: Maternal health risks remain one of the critical challenges in the world, contributing much to maternal and infant morbidity and mortality, especially in the most vulnerable populations. In the modern era, with the recent progress in the area of artificial intelligence and machine learning, much promise has emerged with regard to achieving the goal of early risk detection and its management. This research is set out to relate high-risk, low-risk, and mid-risk maternal health using machine learning algorithms based on physiological data. Materials and Methods: The applied dataset contains 1014 instances (i.e., cases) with seven attributes (i.e., variables), namely, Age, SystolicBP, DiastolicBP, BS, BodyTemp, HeartRate, and RiskLevel. The preprocessed dataset used was then trained and tested with six classifiers using 10-fold cross-validation. Finally, the performance metrics of the models erre compared using metrics like Accuracy, Precision, and the True Positive Rate. Results: The best performance was found for the Random Forest, also reaching the highest values for Accuracy (88.03%), TP Rate (88%), and Precision (88.10%), showing its robustness in handling maternal health risk classification. The mid-risk category was the most challenging across all the models, characterized by lowered Recall and Precision scores, hence underlining class imbalance as one of the bottlenecks in performance. Conclusions: Machine learning algorithms hold strong potential for improving maternal health risk prediction. The findings underline the place of machine learning in advancing maternal healthcare by driving more data-driven and personalized approaches.
Keywords: Random Forests; classification; data-driven healthcare; machine learning; maternal health risk; midwifery; pregnancy care.