HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

TL;DR

HealthEdge is a novel framework integrating IoT, edge, and cloud computing with machine learning to improve Type 2 diabetes prediction accuracy, demonstrating RF's superior performance over LR.

Contribution

The paper introduces a comprehensive IoT-edge-cloud healthcare system for diabetes prediction and compares two machine learning algorithms within this integrated environment.

Findings

Random Forest achieves 6% higher accuracy than Logistic Regression.

RF outperforms LR in diabetes prediction across datasets.

The framework enhances early detection capabilities in smart healthcare systems.

Abstract

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.