Real-time pre-eclampsia prediction model based on IoT and machine learning

Munyao, Michael Muia; Maina, Elizaphan Muuro; Mambo, Shadrack Maina; Wanyoro, Anthony

doi:10.1007/s43926-024-00063-8

Discov Internet Things

2024

DOI: 10.1007/s43926-024-00063-8

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Real-time pre-eclampsia prediction model based on IoT and machine learning

Michael Muia Munyao,

Elizaphan Muuro Maina,

Shadrack Maina Mambo

et al.

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2024

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Cited by 1 publication

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A Digital Twin Framework for Real-Time Healthcare Monitoring: Leveraging AI and Secure Systems for Enhanced Patient Outcomes

Jameil,

Al-Raweshidy

2024

Preprint

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Digital Twin (DT) technology in healthcare is relatively new and faces several challenges, e.g., real-time data processing, secure system integration, and robust cybersecurity. Despite the growing demand for real-time monitoring frameworks, further improvements remain possible. In this study, an architecture has been introduced that utilises cloud computing to create a DT ecosystem. A group of 20 participants has been monitored continuously using high-speed technology to track key physiological parameters, i.e., diabetes risk factors, heart rate (HR), oxygen saturation (SpO2) levels, and body temperature (BT). The DT model functions as a tool, storing both real-time sensor data and historical records, to effectively identify health risks and anomalies. An MLP model was combined with XGBoost, resulting in a 25% reduction in training time and a 33% reduction in testing time. The model demonstrated reliability with an accuracy of 98.9% and achieved real-time accuracy of 95.4%, alongside an F1 score of 0.984. Meticulous attention has been paid to cybersecurity measures, ensuring system integrity through end-to-end encryption and compliance with health data regulations. The incorporation of DT and AI within the healthcare sector is seen as having the potential to overcome existing limitations in monitoring systems, while workloads are relieved and data-driven diagnostics and decision-making processes are improved, e.g., through enhanced real-time patient monitoring and predictive analysis

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A Digital Twin Framework for Real-Time Healthcare Monitoring: Leveraging AI and Secure Systems for Enhanced Patient Outcomes

Jameil,

Al-Raweshidy

2024

Preprint

View full text Add to dashboard Cite

show abstract

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Real-time pre-eclampsia prediction model based on IoT and machine learning

Cited by 1 publication

References 49 publications

A Digital Twin Framework for Real-Time Healthcare Monitoring: Leveraging AI and Secure Systems for Enhanced Patient Outcomes

A Digital Twin Framework for Real-Time Healthcare Monitoring: Leveraging AI and Secure Systems for Enhanced Patient Outcomes

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