PERSONALIZED RISK ASSESSMENT FOR MISCARRIAGE USING MACHINE LEARNING AND WEARABLE DEVICE DATA (KEY IJP************799)

• Sujith Reddy Nalla

Abstract

Miscarriage, a distressing complication of pregnancy, affects millions of women worldwide, often leading to emotional trauma, physical health challenges, and complications in future pregnancies. Early detection and timely intervention are critical to reducing these risks. This project develops a mobile application that leverages machine learning and wearable device data to assess and monitor miscarriage risk in real time. The app collects health and activity data such as heart rate, body temperature, and stress levels from smartwatches and mobile devices at regular intervals. Using predictive models, the system analyses this data to identify potential risks and sends personalized notifications to pregnant women and their caregivers, empowering them to take preventive measures or seek medical assistance. By integrating advanced technology with healthcare, this scalable solution aims to improve maternal health outcomes and reduce miscarriage-related complications.