SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

TL;DR

SmartEdge is an integrated edge-cloud system utilizing ensemble machine learning for real-time diabetes prediction, reducing latency and improving accuracy in IoMT healthcare applications.

Contribution

This paper introduces an end-to-end edge-cloud framework for diabetes prediction, leveraging ensemble ML to enhance accuracy and address latency in IoMT healthcare systems.

Findings

Ensemble ML improves prediction accuracy by 5% over single models.

Edge computing reduces latency in healthcare data processing.

System demonstrates effective deployment on various configurations.

Abstract

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.