Method for Maternal Health Risk Assessment with Smartwatch-Based Vital Sign Measurements

The risk of maternal health issues remains a particular challenge in regions with scant access to continuous antenatal care. This study proposes a smartwatch-based system for evaluating the possible risks associated with maternal health through monitoring vital signs and machine learning algorithms. Using an open-access dataset from Kaggle, the smartwatch assesses maternal risk levels by monitoring systolic and diastolic blood pressure, heart rate, blood glucose, and body temperature. The combination of Artificial Neural Network (ANN) and Random Forest (RF) classifiers gave the system's best-obtained results of 95% accuracy, 97% precision, 97% recall, and an F1 score of 0.97 on the testing dataset. Analysis of correlation demonstrated significant relationships between maternal risk and several primary measures, particularly with systolic blood pressure (r = 0.931), diastolic pressure (r = 0.916), and blood glucose (r = 0.887). Two regression models, MHRL1 and MHRL2, were created to estimate risk levels based on these parameters. From the experimental data, three clinical action levels were defined for the management of pregnancy care: 1) hypertension with Blood Pressure: BP ≥140/90 mmHg, 2) elevated fasting glucose ≥95 mg/dL or postprandial ≥140 mg/dL, and 3) tachycardia with sustained heart rate >100 bpm. These results prove the capability of using IoT-based wearables integrated into workflows for maternal monitoring to enable early warning systems and tailored health management, particularly in constrained settings.