Levraging data analytics for fetal health monitoring
| dc.contributor.author | CETHIVEN REDOΥ 1ΝΗ21AI129, PNUTHAN SAI 1NH21AI075 | |
| dc.date.accessioned | 2025-05-14T04:25:42Z | |
| dc.date.available | 2025-05-14T04:25:42Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The Fetal and Maternal Health Monitoring System integrates real-time data analytics, machine learning (ML), and multi-source data aggregation to improve prenatal care. Traditional methods like cardiotocography (CTG) often fall short in accuracy, real-time monitoring, and predictive capabilities, which can result in missed diagnoses or unnecessary interventions. This system addresses these issues by providing continuous, non-invasive monitoring of fetal heart rate (FHR) and maternal health metrics, enabling early detection of complications such as fetal distress, hypoxia, and preeclampsia. Key goals include enhancing predictive accuracy with ML models, integrating maternal and environmental data, and offering actionable insights through an intuitive user interface. Real-time monitoring and predictive analytics enable timely interventions, improving outcomes. The system uses technologies like convolutional neural networks (CNNs), decision trees, and advanced visualization tools, advancing anomaly detection and personalized care. Its scalable architecture supports integration with electronic health records (EHRs) and existing healthcare infrastructure, ensuring adaptability. Prioritizing data security and compliance with healthcare regulations, this approach fosters a proactive, personalized, and data-driven healthcare ecosystem. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/18865 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Levraging data analytics for fetal health monitoring | |
| dc.type | Learning Object |