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

Alain Hennebelle, Huned Materwala, Leila Ismail
1/25/2023
cs.LGcs.AIq-bio.QM

No official code found. Best implementation: INDUCE-Lab/HealthEdge-Diabetes-Prediction (11 stars, last commit 2023-08-10).

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.

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Code Implementations(1)

INDUCE-Lab/HealthEdge-Diabetes-PredictionCommunity100%

IoT-Edge for Healthcare

117PythonNov 15, 20223 years ago
blockchaincloud-computingiot-edgemachine-learning-algorithms

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Cite this paper

@article{hennebelle2023healthedge,
  title  = {HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System},
  author = {Alain Hennebelle and Huned Materwala and Leila Ismail},
  year   = {2023},
  eprint = {2301.10450},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.10450v1}
}

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